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[ "We no longer host datasets in this repo. You should use the HF Hub instead." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.", "It we could have something similar to Polars, that would be great.\r\n\r\nThis is what Polars outputs: \r\n* `__repr__`/`__str__` :\r\n```\r\nshape: (67_349, 3)\r\n┌───────┬───────────────────────────────────┬───────┐\r\n│ idx ┆ sentence ┆ label │\r\n│ --- ┆ --- ┆ --- │\r\n│ i32 ┆ str ┆ i64 │\r\n╞═══════╪═══════════════════════════════════╪═══════╡\r\n│ 0 ┆ hide new secretions from the par… ┆ 0 │\r\n│ 1 ┆ contains no wit , only labored g… ┆ 0 │\r\n│ 2 ┆ that loves its characters and co… ┆ 1 │\r\n│ 3 ┆ remains utterly satisfied to rem… ┆ 0 │\r\n│ … ┆ … ┆ … │\r\n│ 67345 ┆ anguish , anger and frustration ┆ 0 │\r\n│ 67346 ┆ at achieving the modest , crowd-… ┆ 1 │\r\n│ 67347 ┆ a patient viewer ┆ 1 │\r\n│ 67348 ┆ this new jangle of noise , mayhe… ┆ 0 │\r\n└───────┴───────────────────────────────────┴───────┘\r\n```\r\n\r\n* `_repr_html_`:\r\n<img width=\"251\" alt=\"Screenshot 2023-07-12 at 18 25 58\" src=\"https://github.com/huggingface/datasets/assets/47462742/5d04519d-f302-4411-9fbc-7445bdf53b23\">\r\n\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007633 / 0.011353 (-0.003720) | 0.004666 / 0.011008 (-0.006343) | 0.097768 / 0.038508 (0.059260) | 0.085153 / 0.023109 (0.062044) | 0.400315 / 0.275898 (0.124417) | 0.452903 / 0.323480 (0.129423) | 0.006227 / 0.007986 (-0.001759) | 0.003814 / 0.004328 (-0.000515) | 0.074586 / 0.004250 (0.070336) | 0.064295 / 0.037052 (0.027242) | 0.408082 / 0.258489 (0.149593) | 0.446921 / 0.293841 (0.153080) | 0.034593 / 0.128546 (-0.093953) | 0.009191 / 0.075646 (-0.066456) | 0.337099 / 0.419271 (-0.082173) | 0.075320 / 0.043533 (0.031787) | 0.403488 / 0.255139 (0.148349) | 0.435309 / 0.283200 (0.152109) | 0.035675 / 0.141683 (-0.106008) | 1.732642 / 1.452155 (0.280487) | 1.770238 / 1.492716 (0.277522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235879 / 0.018006 (0.217873) | 0.500330 / 0.000490 (0.499841) | 0.005221 / 0.000200 (0.005021) | 0.000150 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032479 / 0.037411 (-0.004933) | 0.095873 / 0.014526 (0.081348) | 0.107118 / 0.176557 (-0.069438) | 0.173809 / 0.737135 (-0.563326) | 0.109832 / 0.296338 (-0.186507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444342 / 0.215209 (0.229133) | 4.459010 / 2.077655 (2.381355) | 2.209687 / 1.504120 (0.705567) | 2.007556 / 1.541195 (0.466362) | 2.113683 / 1.468490 (0.645193) | 0.544281 / 4.584777 (-4.040496) | 4.037151 / 3.745712 (0.291439) | 4.852644 / 5.269862 (-0.417217) | 3.134126 / 4.565676 (-1.431550) | 0.066815 / 0.424275 (-0.357460) | 0.008836 / 0.007607 (0.001229) | 0.560904 / 0.226044 (0.334859) | 5.302760 / 2.268929 (3.033832) | 2.750182 / 55.444624 (-52.694442) | 2.322595 / 6.876477 (-4.553882) | 2.547486 / 2.142072 (0.405414) | 0.665766 / 4.805227 (-4.139461) | 0.151613 / 6.500664 (-6.349051) | 0.071155 / 0.075469 (-0.004314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473717 / 1.841788 (-0.368071) | 22.584179 / 8.074308 (14.509871) | 15.888001 / 10.191392 (5.696609) | 0.181073 / 0.680424 (-0.499351) | 0.021395 / 0.534201 (-0.512806) | 0.452693 / 0.579283 (-0.126590) | 0.447709 / 0.434364 (0.013345) | 0.529599 / 0.540337 (-0.010738) | 0.699241 / 1.386936 (-0.687695) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007917 / 0.011353 (-0.003436) | 0.004544 / 0.011008 (-0.006464) | 0.074566 / 0.038508 (0.036058) | 0.087530 / 0.023109 (0.064421) | 0.419753 / 0.275898 (0.143854) | 0.452352 / 0.323480 (0.128872) | 0.005882 / 0.007986 (-0.002104) | 0.003904 / 0.004328 (-0.000425) | 0.073539 / 0.004250 (0.069289) | 0.071320 / 0.037052 (0.034267) | 0.432899 / 0.258489 (0.174409) | 0.470365 / 0.293841 (0.176524) | 0.036198 / 0.128546 (-0.092348) | 0.009342 / 0.075646 (-0.066304) | 0.080970 / 0.419271 (-0.338301) | 0.058769 / 0.043533 (0.015236) | 0.413397 / 0.255139 (0.158258) | 0.448362 / 0.283200 (0.165162) | 0.034177 / 0.141683 (-0.107506) | 1.706217 / 1.452155 (0.254063) | 1.776743 / 1.492716 (0.284026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198779 / 0.018006 (0.180773) | 0.499862 / 0.000490 (0.499372) | 0.003891 / 0.000200 (0.003692) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034671 / 0.037411 (-0.002740) | 0.103165 / 0.014526 (0.088639) | 0.115813 / 0.176557 (-0.060744) | 0.177407 / 0.737135 (-0.559728) | 0.117733 / 0.296338 (-0.178606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476859 / 0.215209 (0.261650) | 4.823063 / 2.077655 (2.745409) | 2.524133 / 1.504120 (1.020013) | 2.374482 / 1.541195 (0.833288) | 2.518047 / 1.468490 (1.049557) | 0.559131 / 4.584777 (-4.025646) | 4.126213 / 3.745712 (0.380501) | 6.488570 / 5.269862 (1.218708) | 3.816540 / 4.565676 (-0.749137) | 0.064742 / 0.424275 (-0.359533) | 0.008476 / 0.007607 (0.000869) | 0.576432 / 0.226044 (0.350387) | 5.835133 / 2.268929 (3.566205) | 3.237833 / 55.444624 (-52.206791) | 2.726596 / 6.876477 (-4.149880) | 2.799212 / 2.142072 (0.657139) | 0.661628 / 4.805227 (-4.143599) | 0.153997 / 6.500664 (-6.346667) | 0.070621 / 0.075469 (-0.004848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648505 / 1.841788 (-0.193282) | 22.454019 / 8.074308 (14.379711) | 16.077098 / 10.191392 (5.885706) | 0.217875 / 0.680424 (-0.462549) | 0.021285 / 0.534201 (-0.512916) | 0.459837 / 0.579283 (-0.119446) | 0.476211 / 0.434364 (0.041847) | 0.525903 / 0.540337 (-0.014435) | 0.717224 / 1.386936 (-0.669712) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008929 / 0.011353 (-0.002424) | 0.004188 / 0.011008 (-0.006820) | 0.097030 / 0.038508 (0.058522) | 0.071363 / 0.023109 (0.048254) | 0.333116 / 0.275898 (0.057218) | 0.371272 / 0.323480 (0.047792) | 0.006430 / 0.007986 (-0.001555) | 0.003689 / 0.004328 (-0.000639) | 0.068666 / 0.004250 (0.064416) | 0.057562 / 0.037052 (0.020510) | 0.347208 / 0.258489 (0.088719) | 0.390514 / 0.293841 (0.096673) | 0.050560 / 0.128546 (-0.077987) | 0.013372 / 0.075646 (-0.062275) | 0.311345 / 0.419271 (-0.107927) | 0.068990 / 0.043533 (0.025457) | 0.363026 / 0.255139 (0.107887) | 0.379793 / 0.283200 (0.096593) | 0.036891 / 0.141683 (-0.104792) | 1.583481 / 1.452155 (0.131327) | 1.688727 / 1.492716 (0.196011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209777 / 0.018006 (0.191771) | 0.507267 / 0.000490 (0.506777) | 0.003637 / 0.000200 (0.003438) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029309 / 0.037411 (-0.008102) | 0.088386 / 0.014526 (0.073861) | 0.104974 / 0.176557 (-0.071582) | 0.171999 / 0.737135 (-0.565137) | 0.110797 / 0.296338 (-0.185542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543465 / 0.215209 (0.328256) | 5.361491 / 2.077655 (3.283836) | 2.348712 / 1.504120 (0.844592) | 2.012527 / 1.541195 (0.471332) | 2.069776 / 1.468490 (0.601286) | 0.874262 / 4.584777 (-3.710515) | 4.877317 / 3.745712 (1.131605) | 5.327459 / 5.269862 (0.057597) | 3.336823 / 4.565676 (-1.228854) | 0.100456 / 0.424275 (-0.323819) | 0.008503 / 0.007607 (0.000895) | 0.692009 / 0.226044 (0.465965) | 6.912731 / 2.268929 (4.643802) | 3.110548 / 55.444624 (-52.334076) | 2.443665 / 6.876477 (-4.432811) | 2.528713 / 2.142072 (0.386641) | 1.076358 / 4.805227 (-3.728869) | 0.220352 / 6.500664 (-6.280312) | 0.080293 / 0.075469 (0.004824) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538444 / 1.841788 (-0.303344) | 21.121221 / 8.074308 (13.046913) | 19.810609 / 10.191392 (9.619216) | 0.225406 / 0.680424 (-0.455018) | 0.026652 / 0.534201 (-0.507549) | 0.430372 / 0.579283 (-0.148911) | 0.510722 / 0.434364 (0.076358) | 0.514347 / 0.540337 (-0.025991) | 0.686050 / 1.386936 (-0.700886) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007675 / 0.011353 (-0.003678) | 0.004542 / 0.011008 (-0.006466) | 0.069655 / 0.038508 (0.031147) | 0.069338 / 0.023109 (0.046229) | 0.436505 / 0.275898 (0.160607) | 0.481806 / 0.323480 (0.158326) | 0.005315 / 0.007986 (-0.002670) | 0.004455 / 0.004328 (0.000127) | 0.072674 / 0.004250 (0.068424) | 0.058088 / 0.037052 (0.021035) | 0.445825 / 0.258489 (0.187336) | 0.501706 / 0.293841 (0.207865) | 0.047123 / 0.128546 (-0.081424) | 0.012943 / 0.075646 (-0.062703) | 0.093491 / 0.419271 (-0.325780) | 0.060169 / 0.043533 (0.016637) | 0.436530 / 0.255139 (0.181391) | 0.466873 / 0.283200 (0.183674) | 0.040453 / 0.141683 (-0.101230) | 1.586438 / 1.452155 (0.134283) | 1.671081 / 1.492716 (0.178365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180607 / 0.018006 (0.162601) | 0.520145 / 0.000490 (0.519655) | 0.004824 / 0.000200 (0.004624) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029308 / 0.037411 (-0.008103) | 0.093652 / 0.014526 (0.079126) | 0.102332 / 0.176557 (-0.074224) | 0.162414 / 0.737135 (-0.574721) | 0.098017 / 0.296338 (-0.198321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583949 / 0.215209 (0.368740) | 6.035191 / 2.077655 (3.957536) | 2.801274 / 1.504120 (1.297155) | 2.566150 / 1.541195 (1.024955) | 2.437122 / 1.468490 (0.968632) | 0.865038 / 4.584777 (-3.719739) | 4.841727 / 3.745712 (1.096015) | 4.683919 / 5.269862 (-0.585943) | 2.941240 / 4.565676 (-1.624437) | 0.104888 / 0.424275 (-0.319387) | 0.007747 / 0.007607 (0.000140) | 0.780041 / 0.226044 (0.553997) | 7.771314 / 2.268929 (5.502385) | 3.680814 / 55.444624 (-51.763811) | 2.938472 / 6.876477 (-3.938004) | 2.981740 / 2.142072 (0.839668) | 1.065411 / 4.805227 (-3.739816) | 0.222265 / 6.500664 (-6.278399) | 0.082428 / 0.075469 (0.006959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.626774 / 1.841788 (-0.215014) | 21.618284 / 8.074308 (13.543976) | 20.596743 / 10.191392 (10.405351) | 0.240969 / 0.680424 (-0.439454) | 0.025630 / 0.534201 (-0.508570) | 0.481981 / 0.579283 (-0.097302) | 0.547914 / 0.434364 (0.113550) | 0.522296 / 0.540337 (-0.018041) | 0.729174 / 1.386936 (-0.657762) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]

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[ "Hi ! The huggingface/dataset-viewer code was not maintained anymore because we switched to a new dataset viewer that is deployed available for each dataset the Hugging Face website.\r\n\r\nWhat are you using this old repository for ?", "I think these parts are outdated:\r\n\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L126-L131\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L145-L150\r\n\r\nTo make the viewer work, the first one should be replaced with the following:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nconfs = builder_cls.BUILDER_CONFIGS\r\n```\r\nAnd the second one:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nif conf:\r\n builder_instance = builder_cls(name=conf, cache_dir=path if path_to_datasets is not None else None)\r\nelse:\r\n builder_instance = builder_cls(cache_dir=path if path_to_datasets is not None else None)\r\n```\r\n\r\nBut as @lhoestq suggested, it's better to use the `datasets-server` API nowadays to [fetch the rows](https://huggingface.co/docs/datasets-server/rows).", "> The dataset viewer on the Hugging Face website is incredibly useful\r\n\r\n@mariosasko i think @lilyorlilypad wants to run the new dataset-viewer, not the old one", "> wants to run the new dataset-viewer, not the old one\r\n\r\nThanks for the clarification for me. I do want to run the new dataset-viewer. ", "It should be possible to run it locally using the HF datasets-server API (docs [here](https://huggingface.co/docs/datasets-server)) but the front end part is not open source (yet ?)\r\n\r\nThe back-end is open source though if you're interested: https://github.com/huggingface/datasets-server\r\nIt automatically converts datasets on HF to Parquet, which is the format we use to power the viewer.", "the new frontend would probably be hard to open source, as is, as it's quite intertwined with the Hub's code.\r\n\r\nHowever, at some point it would be amazing to have a community-driven open source implementation of a frontend to datasets-server! " ]

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[ "You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize." ]

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[ "You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ", "> You can use `with_transform` to get a new dataset object.\r\n> \r\n> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.\r\n\r\nI read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\n`with_transform` still does not chain AFAIU.", "> I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\nYou must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:\r\n```python\r\nfrom functools import partial\r\nfrom datasets import Dataset\r\n\r\ndef gen_from_iterable_dataset(iterable_ds)\r\n yield from iterable_ds\r\n\r\nds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n```\r\n\r\n> with_transform still does not chain AFAIU.\r\n\r\nYes, not supported yet - the solution is to combine the transforms into a single one.", "I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ?", "> ```python\r\n> from functools import partial\r\n> from datasets import Dataset\r\n> \r\n> def gen_from_iterable_dataset(iterable_ds)\r\n> yield from iterable_ds\r\n> \r\n> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n> ```\r\n\r\n@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?\r\n", "The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash)" ]

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[ "Hi! You can do `ds.get_index(\"embeddings\").faiss_index.metric_type` to get the metric type and then match the result with the FAISS metric [enum](https://github.com/facebookresearch/faiss/blob/43d86e30736ede853c384b24667fc3ab897d6ba9/faiss/MetricType.h#L22-L36) (should be L2).", "Ah! Thank you for pointing this out. FYI: the enum indicates it's using the inner product. Using `torch.inner` or `torch.dot` still produces a discrepancy compared to the built-in score. I think this is because of the compression/quantization that occurs with the FAISS index." ]

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[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006961 / 0.011353 (-0.004392) | 0.004390 / 0.011008 (-0.006618) | 0.103249 / 0.038508 (0.064741) | 0.048084 / 0.023109 (0.024975) | 0.351213 / 0.275898 (0.075315) | 0.416918 / 0.323480 (0.093439) | 0.005539 / 0.007986 (-0.002446) | 0.003555 / 0.004328 (-0.000774) | 0.079306 / 0.004250 (0.075055) | 0.066937 / 0.037052 (0.029884) | 0.382601 / 0.258489 (0.124112) | 0.406125 / 0.293841 (0.112284) | 0.032269 / 0.128546 (-0.096277) | 0.009133 / 0.075646 (-0.066514) | 0.354449 / 0.419271 (-0.064822) | 0.068978 / 0.043533 (0.025445) | 0.352314 / 0.255139 (0.097175) | 0.390398 / 0.283200 (0.107199) | 0.025640 / 0.141683 (-0.116043) | 1.553865 / 1.452155 (0.101710) | 1.601292 / 1.492716 (0.108576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208310 / 0.018006 (0.190303) | 0.440076 / 0.000490 (0.439586) | 0.000363 / 0.000200 (0.000163) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029173 / 0.037411 (-0.008238) | 0.111323 / 0.014526 (0.096797) | 0.123001 / 0.176557 (-0.053556) | 0.180180 / 0.737135 (-0.556955) | 0.125804 / 0.296338 (-0.170534) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419919 / 0.215209 (0.204710) | 4.194515 / 2.077655 (2.116860) | 1.881234 / 1.504120 (0.377114) | 1.672914 / 1.541195 (0.131720) | 1.723102 / 1.468490 (0.254612) | 0.543584 / 4.584777 (-4.041193) | 3.822477 / 3.745712 (0.076765) | 1.837946 / 5.269862 (-3.431915) | 1.094975 / 4.565676 (-3.470701) | 0.066788 / 0.424275 (-0.357487) | 0.011689 / 0.007607 (0.004082) | 0.520983 / 0.226044 (0.294938) | 5.209245 / 2.268929 (2.940316) | 2.392916 / 55.444624 (-53.051708) | 2.060042 / 6.876477 (-4.816434) | 2.162291 / 2.142072 (0.020219) | 0.668472 / 4.805227 (-4.136755) | 0.144373 / 6.500664 (-6.356291) | 0.066152 / 0.075469 (-0.009318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251256 / 1.841788 (-0.590532) | 15.161338 / 8.074308 (7.087030) | 14.416133 / 10.191392 (4.224741) | 0.166145 / 0.680424 (-0.514279) | 0.018168 / 0.534201 (-0.516033) | 0.433364 / 0.579283 (-0.145919) | 0.417484 / 0.434364 (-0.016880) | 0.502543 / 0.540337 (-0.037794) | 0.602904 / 1.386936 (-0.784032) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006946 / 0.011353 (-0.004407) | 0.004248 / 0.011008 (-0.006761) | 0.079707 / 0.038508 (0.041199) | 0.046226 / 0.023109 (0.023117) | 0.375864 / 0.275898 (0.099966) | 0.430740 / 0.323480 (0.107260) | 0.006222 / 0.007986 (-0.001764) | 0.003474 / 0.004328 (-0.000854) | 0.079622 / 0.004250 (0.075372) | 0.066666 / 0.037052 (0.029613) | 0.379487 / 0.258489 (0.120998) | 0.423002 / 0.293841 (0.129161) | 0.032836 / 0.128546 (-0.095710) | 0.008976 / 0.075646 (-0.066670) | 0.086578 / 0.419271 (-0.332693) | 0.055651 / 0.043533 (0.012118) | 0.360787 / 0.255139 (0.105648) | 0.384265 / 0.283200 (0.101065) | 0.025350 / 0.141683 (-0.116333) | 1.547880 / 1.452155 (0.095725) | 1.605850 / 1.492716 (0.113134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184227 / 0.018006 (0.166220) | 0.442071 / 0.000490 (0.441582) | 0.002887 / 0.000200 (0.002687) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031923 / 0.037411 (-0.005488) | 0.119093 / 0.014526 (0.104568) | 0.128704 / 0.176557 (-0.047853) | 0.187065 / 0.737135 (-0.550070) | 0.134135 / 0.296338 (-0.162204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455731 / 0.215209 (0.240522) | 4.562911 / 2.077655 (2.485256) | 2.247431 / 1.504120 (0.743311) | 2.053346 / 1.541195 (0.512151) | 2.049611 / 1.468490 (0.581121) | 0.546069 / 4.584777 (-4.038708) | 3.821852 / 3.745712 (0.076140) | 3.358497 / 5.269862 (-1.911364) | 1.667697 / 4.565676 (-2.897979) | 0.067968 / 0.424275 (-0.356307) | 0.012344 / 0.007607 (0.004737) | 0.550864 / 0.226044 (0.324820) | 5.496867 / 2.268929 (3.227939) | 2.680031 / 55.444624 (-52.764594) | 2.328673 / 6.876477 (-4.547804) | 2.436754 / 2.142072 (0.294682) | 0.681195 / 4.805227 (-4.124033) | 0.148761 / 6.500664 (-6.351904) | 0.067716 / 0.075469 (-0.007753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353798 / 1.841788 (-0.487990) | 15.992965 / 8.074308 (7.918657) | 14.051539 / 10.191392 (3.860147) | 0.181087 / 0.680424 (-0.499337) | 0.018653 / 0.534201 (-0.515548) | 0.433499 / 0.579283 (-0.145784) | 0.428845 / 0.434364 (-0.005519) | 0.501100 / 0.540337 (-0.039238) | 0.603666 / 1.386936 (-0.783270) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10cfa871a2f387fe9c6360e1873ea74c6d69ff67 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010983 / 0.011353 (-0.000370) | 0.005630 / 0.011008 (-0.005378) | 0.109967 / 0.038508 (0.071458) | 0.101580 / 0.023109 (0.078471) | 0.490205 / 0.275898 (0.214307) | 0.534653 / 0.323480 (0.211173) | 0.008365 / 0.007986 (0.000379) | 0.004317 / 0.004328 (-0.000012) | 0.082429 / 0.004250 (0.078179) | 0.080556 / 0.037052 (0.043504) | 0.494627 / 0.258489 (0.236138) | 0.544189 / 0.293841 (0.250348) | 0.049419 / 0.128546 (-0.079127) | 0.014033 / 0.075646 (-0.061613) | 0.370406 / 0.419271 (-0.048866) | 0.083468 / 0.043533 (0.039935) | 0.463829 / 0.255139 (0.208690) | 0.507516 / 0.283200 (0.224316) | 0.053266 / 0.141683 (-0.088417) | 1.778680 / 1.452155 (0.326525) | 1.916616 / 1.492716 (0.423900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267646 / 0.018006 (0.249640) | 0.617824 / 0.000490 (0.617334) | 0.007720 / 0.000200 (0.007520) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034464 / 0.037411 (-0.002948) | 0.113626 / 0.014526 (0.099100) | 0.118911 / 0.176557 (-0.057646) | 0.194701 / 0.737135 (-0.542434) | 0.123431 / 0.296338 (-0.172907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606073 / 0.215209 (0.390863) | 6.086393 / 2.077655 (4.008738) | 2.568712 / 1.504120 (1.064593) | 2.260801 / 1.541195 (0.719606) | 2.411798 / 1.468490 (0.943307) | 0.876433 / 4.584777 (-3.708344) | 5.521280 / 3.745712 (1.775568) | 5.969722 / 5.269862 (0.699861) | 3.671028 / 4.565676 (-0.894649) | 0.097082 / 0.424275 (-0.327193) | 0.011354 / 0.007607 (0.003747) | 0.713842 / 0.226044 (0.487798) | 7.291172 / 2.268929 (5.022244) | 3.315272 / 55.444624 (-52.129352) | 2.777487 / 6.876477 (-4.098990) | 3.025449 / 2.142072 (0.883377) | 1.014115 / 4.805227 (-3.791112) | 0.217928 / 6.500664 (-6.282736) | 0.083097 / 0.075469 (0.007627) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640060 / 1.841788 (-0.201728) | 25.342172 / 8.074308 (17.267864) | 22.776510 / 10.191392 (12.585118) | 0.227300 / 0.680424 (-0.453124) | 0.032233 / 0.534201 (-0.501968) | 0.507547 / 0.579283 (-0.071736) | 0.647044 / 0.434364 (0.212680) | 0.607019 / 0.540337 (0.066682) | 0.823548 / 1.386936 (-0.563388) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009576 / 0.011353 (-0.001777) | 0.009322 / 0.011008 (-0.001687) | 0.087184 / 0.038508 (0.048676) | 0.100795 / 0.023109 (0.077685) | 0.492138 / 0.275898 (0.216240) | 0.528386 / 0.323480 (0.204906) | 0.006689 / 0.007986 (-0.001296) | 0.004735 / 0.004328 (0.000406) | 0.085519 / 0.004250 (0.081269) | 0.072648 / 0.037052 (0.035595) | 0.496068 / 0.258489 (0.237579) | 0.549634 / 0.293841 (0.255793) | 0.049709 / 0.128546 (-0.078837) | 0.015077 / 0.075646 (-0.060569) | 0.099445 / 0.419271 (-0.319826) | 0.068080 / 0.043533 (0.024547) | 0.500426 / 0.255139 (0.245287) | 0.531437 / 0.283200 (0.248238) | 0.053176 / 0.141683 (-0.088507) | 1.827942 / 1.452155 (0.375787) | 1.914286 / 1.492716 (0.421570) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247658 / 0.018006 (0.229652) | 0.590805 / 0.000490 (0.590315) | 0.005319 / 0.000200 (0.005119) | 0.000165 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036993 / 0.037411 (-0.000418) | 0.112944 / 0.014526 (0.098419) | 0.118964 / 0.176557 (-0.057593) | 0.194867 / 0.737135 (-0.542269) | 0.120816 / 0.296338 (-0.175523) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.638062 / 0.215209 (0.422853) | 6.246785 / 2.077655 (4.169130) | 2.957779 / 1.504120 (1.453659) | 2.739118 / 1.541195 (1.197924) | 2.795362 / 1.468490 (1.326872) | 0.890532 / 4.584777 (-3.694245) | 5.508198 / 3.745712 (1.762486) | 5.222315 / 5.269862 (-0.047547) | 3.152731 / 4.565676 (-1.412946) | 0.098344 / 0.424275 (-0.325931) | 0.008800 / 0.007607 (0.001193) | 0.757889 / 0.226044 (0.531845) | 7.545715 / 2.268929 (5.276787) | 3.694536 / 55.444624 (-51.750088) | 3.112872 / 6.876477 (-3.763605) | 3.182358 / 2.142072 (1.040285) | 1.028171 / 4.805227 (-3.777056) | 0.215223 / 6.500664 (-6.285441) | 0.085856 / 0.075469 (0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.853138 / 1.841788 (0.011350) | 25.939672 / 8.074308 (17.865364) | 23.118029 / 10.191392 (12.926637) | 0.250599 / 0.680424 (-0.429825) | 0.029942 / 0.534201 (-0.504259) | 0.508748 / 0.579283 (-0.070535) | 0.593966 / 0.434364 (0.159602) | 0.605499 / 0.540337 (0.065162) | 0.863827 / 1.386936 (-0.523109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d15950d99677e9473cdcd31cfd83aa17e313e28 \"CML watermark\")\n" ]

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[ "By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n\r\nOur support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.", "> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n> \r\n> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.\r\n\r\nThanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?", "It's best to use the `datasets.Image()` feature type for PIL images (to save space) :)" ]

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[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.", "I am afraid int32 is not the reason for this error.\r\n\r\nI have submitted a commit to use int64 for all ints in the dataset:\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/857ac00d9eab96a6708ad6a82bd9001686042a9e\r\n\r\nand I have updated my env to the latest datasets release:\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 2.13.1\r\n- Platform: macOS-13.2.1-arm64-arm-64bit\r\n- Python version: 3.11.2\r\n- Huggingface_hub version: 0.13.4\r\n- PyArrow version: 11.0.0\r\n- Pandas version: 1.5.3\r\n\r\nBut the error still exist\r\n\r\n```\r\nDownloading and preparing dataset mnbvc/news_peoples_daily to /Users/silver/.cache/huggingface/datasets/liwu___mnbvc/news_peoples_daily/0.0.1/ee380f6309fe9b8b0d1fb14d77118f132444f22c8c4b28bf5c1645312688e051...\r\nDownloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 9070.40it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 2697.16it/s]\r\n---------------------------------------------------------------------------\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1647, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1646 example = self.info.features.encode_example(record) if self.info.features is not None else record\r\n-> 1647 writer.write(example, key)\r\n 1648 num_examples_progress_update += 1\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:490, in ArrowWriter.write(self, example, key, writer_batch_size)\r\n 488 self.hkey_record = []\r\n--> 490 self.write_examples_on_file()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1656, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1655 num_shards = shard_id + 1\r\n-> 1656 num_examples, num_bytes = writer.finalize()\r\n 1657 writer.close()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)\r\n 583 self.hkey_record = []\r\n--> 584 self.write_examples_on_file()\r\n 585 # If schema is known, infer features even if no examples were written\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\nCell In[2], line 1\r\n----> 1 dataset = load_dataset(\"liwu/MNBVC\", 'news_peoples_daily', split='train')\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/load.py:1809, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1808 # Download and prepare data\r\n-> 1809 builder_instance.download_and_prepare(\r\n 1810 download_config=download_config,\r\n 1811 download_mode=download_mode,\r\n 1812 verification_mode=verification_mode,\r\n 1813 try_from_hf_gcs=try_from_hf_gcs,\r\n 1814 num_proc=num_proc,\r\n 1815 storage_options=storage_options,\r\n 1816 )\r\n 1818 # Build dataset for splits\r\n 1819 keep_in_memory = (\r\n 1820 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1821 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:909, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\r\n 907 if num_proc is not None:\r\n 908 prepare_split_kwargs[\"num_proc\"] = num_proc\r\n--> 909 self._download_and_prepare(\r\n 910 dl_manager=dl_manager,\r\n 911 verification_mode=verification_mode,\r\n 912 **prepare_split_kwargs,\r\n 913 **download_and_prepare_kwargs,\r\n 914 )\r\n 915 # Sync info\r\n 916 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1670, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)\r\n 1669 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):\r\n-> 1670 super()._download_and_prepare(\r\n 1671 dl_manager,\r\n 1672 verification_mode,\r\n 1673 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS\r\n 1674 or verification_mode == VerificationMode.ALL_CHECKS,\r\n 1675 **prepare_splits_kwargs,\r\n 1676 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1004, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)\r\n 1000 split_dict.add(split_generator.split_info)\r\n 1002 try:\r\n 1003 # Prepare split will record examples associated to the split\r\n-> 1004 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 1005 except OSError as e:\r\n 1006 raise OSError(\r\n 1007 \"Cannot find data file. \"\r\n 1008 + (self.manual_download_instructions or \"\")\r\n 1009 + \"\\nOriginal error:\\n\"\r\n 1010 + str(e)\r\n 1011 ) from None\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1508, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\r\n 1506 job_id = 0\r\n 1507 with pbar:\r\n-> 1508 for job_id, done, content in self._prepare_split_single(\r\n 1509 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args\r\n 1510 ):\r\n 1511 if done:\r\n 1512 result = content\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1665, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1663 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:\r\n 1664 e = e.__context__\r\n-> 1665 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1667 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nBesides, it works fine when I am using streamed dataset.", "`simhash` is the problematic column - it has values such as `18329103420363166823` that are out of the int64 range. You can fix this by setting the feature type to `Value(\"string\")` (it's advised to use this type for hash values in general)\r\n\r\n> Besides, it works fine when I am using streamed dataset.\r\n\r\nStreaming yields Python dictionaries from the script without converting them to the Arrow representation, as this conversion step is not that cheap performance-wise.", "i am using uint64 for simhash\r\n\r\nuint64 ranges up to about 3.69E19.\r\n\r\n18329103420363166823 is less than this value.\r\n\r\nmoreover, our simhash algorithm use 64 bits. it should fit in uint64.\r\n\r\n\r\n\r\n", "You are right. I overlooked the feature type.\r\n\r\nThis is a reproducer:\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.arrow_writer import TypedSequence\r\n\r\npa.array(TypedSequence([18329103420363166823], type=Value(\"uint64\")))\r\n```\r\n\r\n`pa.array([18329103420363166823])` also fails with the same error, so it seems PyArrow does not always infer the correct type as NumPy does (`uint64` in this case).\r\n\r\nI'll report this issue in the Arrow repo.\r\n\r\n`pa.array([18329103420363166823], pa.uint64)` works, so maybe we can implement a temporary fix (supporting complex input such as `[{\"image\": pil_image, \"num\": uint64_value}]` would be hard though).\r\n\r\nIn the meantime, you should be able to bypass this error by returning the `simhash` values as NumPy scalars in the script:\r\n```python\r\ndef _generate_examples(self, ...):\r\n ...\r\n yield {..., \"simhash\": np.uint64(simhash), ...}\r\n```", "Thank you for checking this issue in detail.\r\n\r\nHowever, it seems that using `np.uint64(simhash)` does not work. The same issue still exists.\r\n\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/1e44f1e400b7e61052647d44c99cdae3bae9c830\r\n\r\nAnyway, we decide to use string type for these simhash values. Hope pyarrow can fix their bug soon.", "Arrow issue: https://github.com/apache/arrow/issues/36520" ]

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[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006152 / 0.011353 (-0.005200) | 0.003916 / 0.011008 (-0.007092) | 0.097355 / 0.038508 (0.058847) | 0.037228 / 0.023109 (0.014119) | 0.315753 / 0.275898 (0.039855) | 0.387949 / 0.323480 (0.064470) | 0.004804 / 0.007986 (-0.003181) | 0.002975 / 0.004328 (-0.001353) | 0.076932 / 0.004250 (0.072682) | 0.053497 / 0.037052 (0.016445) | 0.331143 / 0.258489 (0.072654) | 0.388347 / 0.293841 (0.094506) | 0.027535 / 0.128546 (-0.101011) | 0.008509 / 0.075646 (-0.067137) | 0.312639 / 0.419271 (-0.106632) | 0.047212 / 0.043533 (0.003679) | 0.316875 / 0.255139 (0.061736) | 0.352191 / 0.283200 (0.068992) | 0.021380 / 0.141683 (-0.120303) | 1.541401 / 1.452155 (0.089247) | 1.519420 / 1.492716 (0.026704) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206332 / 0.018006 (0.188326) | 0.412252 / 0.000490 (0.411762) | 0.005119 / 0.000200 (0.004919) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023856 / 0.037411 (-0.013556) | 0.098216 / 0.014526 (0.083691) | 0.106553 / 0.176557 (-0.070003) | 0.168767 / 0.737135 (-0.568369) | 0.109244 / 0.296338 (-0.187094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457580 / 0.215209 (0.242371) | 4.583246 / 2.077655 (2.505591) | 2.296356 / 1.504120 (0.792236) | 2.096216 / 1.541195 (0.555021) | 2.159086 / 1.468490 (0.690596) | 0.557905 / 4.584777 (-4.026872) | 3.345910 / 3.745712 (-0.399802) | 1.767436 / 5.269862 (-3.502426) | 1.021583 / 4.565676 (-3.544094) | 0.067265 / 0.424275 (-0.357011) | 0.011411 / 0.007607 (0.003804) | 0.559841 / 0.226044 (0.333797) | 5.586892 / 2.268929 (3.317963) | 2.735520 / 55.444624 (-52.709104) | 2.429393 / 6.876477 (-4.447084) | 2.544901 / 2.142072 (0.402829) | 0.667603 / 4.805227 (-4.137625) | 0.136244 / 6.500664 (-6.364421) | 0.066961 / 0.075469 (-0.008508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206529 / 1.841788 (-0.635259) | 13.988306 / 8.074308 (5.913998) | 13.481813 / 10.191392 (3.290421) | 0.161901 / 0.680424 (-0.518523) | 0.016850 / 0.534201 (-0.517351) | 0.367657 / 0.579283 (-0.211626) | 0.393343 / 0.434364 (-0.041021) | 0.465288 / 0.540337 (-0.075050) | 0.559888 / 1.386936 (-0.827048) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005956 / 0.011353 (-0.005397) | 0.003734 / 0.011008 (-0.007274) | 0.077841 / 0.038508 (0.039333) | 0.036532 / 0.023109 (0.013422) | 0.438923 / 0.275898 (0.163025) | 0.490133 / 0.323480 (0.166653) | 0.004651 / 0.007986 (-0.003335) | 0.002881 / 0.004328 (-0.001448) | 0.077868 / 0.004250 (0.073618) | 0.051700 / 0.037052 (0.014647) | 0.448018 / 0.258489 (0.189529) | 0.500304 / 0.293841 (0.206464) | 0.029051 / 0.128546 (-0.099496) | 0.008498 / 0.075646 (-0.067148) | 0.082932 / 0.419271 (-0.336339) | 0.043665 / 0.043533 (0.000132) | 0.431613 / 0.255139 (0.176474) | 0.458749 / 0.283200 (0.175549) | 0.021951 / 0.141683 (-0.119731) | 1.556043 / 1.452155 (0.103888) | 1.588391 / 1.492716 (0.095675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220674 / 0.018006 (0.202667) | 0.415408 / 0.000490 (0.414918) | 0.002613 / 0.000200 (0.002413) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025548 / 0.037411 (-0.011863) | 0.103633 / 0.014526 (0.089107) | 0.115193 / 0.176557 (-0.061364) | 0.163971 / 0.737135 (-0.573164) | 0.114754 / 0.296338 (-0.181585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456823 / 0.215209 (0.241614) | 4.569950 / 2.077655 (2.492296) | 2.196339 / 1.504120 (0.692219) | 1.985822 / 1.541195 (0.444628) | 2.044083 / 1.468490 (0.575593) | 0.567919 / 4.584777 (-4.016858) | 3.397515 / 3.745712 (-0.348197) | 1.741087 / 5.269862 (-3.528775) | 1.041237 / 4.565676 (-3.524440) | 0.068963 / 0.424275 (-0.355313) | 0.011677 / 0.007607 (0.004070) | 0.565010 / 0.226044 (0.338966) | 5.625886 / 2.268929 (3.356957) | 2.670658 / 55.444624 (-52.773967) | 2.300279 / 6.876477 (-4.576198) | 2.392178 / 2.142072 (0.250106) | 0.680226 / 4.805227 (-4.125001) | 0.139119 / 6.500664 (-6.361545) | 0.067953 / 0.075469 (-0.007516) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303280 / 1.841788 (-0.538507) | 14.458686 / 8.074308 (6.384378) | 14.409369 / 10.191392 (4.217977) | 0.144581 / 0.680424 (-0.535843) | 0.016634 / 0.534201 (-0.517567) | 0.364607 / 0.579283 (-0.214676) | 0.394521 / 0.434364 (-0.039843) | 0.433417 / 0.540337 (-0.106921) | 0.527127 / 1.386936 (-0.859809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.003871 / 0.011008 (-0.007138) | 0.098823 / 0.038508 (0.060315) | 0.039853 / 0.023109 (0.016744) | 0.314989 / 0.275898 (0.039091) | 0.376733 / 0.323480 (0.053254) | 0.004754 / 0.007986 (-0.003232) | 0.002971 / 0.004328 (-0.001357) | 0.078451 / 0.004250 (0.074201) | 0.053160 / 0.037052 (0.016107) | 0.324443 / 0.258489 (0.065954) | 0.361488 / 0.293841 (0.067647) | 0.027942 / 0.128546 (-0.100604) | 0.008535 / 0.075646 (-0.067111) | 0.315526 / 0.419271 (-0.103745) | 0.045706 / 0.043533 (0.002174) | 0.329614 / 0.255139 (0.074475) | 0.336339 / 0.283200 (0.053139) | 0.021278 / 0.141683 (-0.120405) | 1.529710 / 1.452155 (0.077555) | 1.566833 / 1.492716 (0.074116) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215263 / 0.018006 (0.197257) | 0.440320 / 0.000490 (0.439830) | 0.002627 / 0.000200 (0.002427) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023971 / 0.037411 (-0.013441) | 0.100549 / 0.014526 (0.086023) | 0.106995 / 0.176557 (-0.069561) | 0.169630 / 0.737135 (-0.567505) | 0.111614 / 0.296338 (-0.184724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424911 / 0.215209 (0.209702) | 4.246920 / 2.077655 (2.169266) | 1.923321 / 1.504120 (0.419202) | 1.714795 / 1.541195 (0.173600) | 1.772906 / 1.468490 (0.304416) | 0.554676 / 4.584777 (-4.030101) | 3.478896 / 3.745712 (-0.266816) | 2.800494 / 5.269862 (-2.469368) | 1.382630 / 4.565676 (-3.183047) | 0.067271 / 0.424275 (-0.357004) | 0.010967 / 0.007607 (0.003360) | 0.526769 / 0.226044 (0.300725) | 5.288564 / 2.268929 (3.019636) | 2.337459 / 55.444624 (-53.107165) | 1.999975 / 6.876477 (-4.876502) | 2.102680 / 2.142072 (-0.039392) | 0.672181 / 4.805227 (-4.133046) | 0.135097 / 6.500664 (-6.365567) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264365 / 1.841788 (-0.577423) | 14.282440 / 8.074308 (6.208132) | 14.220200 / 10.191392 (4.028808) | 0.139055 / 0.680424 (-0.541369) | 0.016681 / 0.534201 (-0.517520) | 0.367936 / 0.579283 (-0.211348) | 0.393959 / 0.434364 (-0.040404) | 0.424438 / 0.540337 (-0.115900) | 0.508065 / 1.386936 (-0.878872) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006514 / 0.011353 (-0.004839) | 0.003890 / 0.011008 (-0.007118) | 0.078871 / 0.038508 (0.040363) | 0.038080 / 0.023109 (0.014971) | 0.358282 / 0.275898 (0.082384) | 0.430654 / 0.323480 (0.107174) | 0.005712 / 0.007986 (-0.002273) | 0.003030 / 0.004328 (-0.001299) | 0.078636 / 0.004250 (0.074386) | 0.057771 / 0.037052 (0.020719) | 0.368814 / 0.258489 (0.110325) | 0.437047 / 0.293841 (0.143206) | 0.029470 / 0.128546 (-0.099076) | 0.008523 / 0.075646 (-0.067124) | 0.083334 / 0.419271 (-0.335938) | 0.044505 / 0.043533 (0.000972) | 0.357484 / 0.255139 (0.102345) | 0.393839 / 0.283200 (0.110639) | 0.023340 / 0.141683 (-0.118343) | 1.561033 / 1.452155 (0.108878) | 1.595560 / 1.492716 (0.102844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204149 / 0.018006 (0.186143) | 0.442747 / 0.000490 (0.442257) | 0.003105 / 0.000200 (0.002905) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027002 / 0.037411 (-0.010409) | 0.105595 / 0.014526 (0.091070) | 0.108695 / 0.176557 (-0.067861) | 0.163182 / 0.737135 (-0.573953) | 0.114999 / 0.296338 (-0.181339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483713 / 0.215209 (0.268504) | 4.836063 / 2.077655 (2.758409) | 2.488072 / 1.504120 (0.983952) | 2.289556 / 1.541195 (0.748361) | 2.342912 / 1.468490 (0.874422) | 0.565937 / 4.584777 (-4.018840) | 3.479085 / 3.745712 (-0.266627) | 1.770922 / 5.269862 (-3.498940) | 1.046084 / 4.565676 (-3.519592) | 0.067857 / 0.424275 (-0.356418) | 0.011283 / 0.007607 (0.003676) | 0.592966 / 0.226044 (0.366921) | 5.932842 / 2.268929 (3.663914) | 2.956252 / 55.444624 (-52.488372) | 2.602704 / 6.876477 (-4.273772) | 2.715625 / 2.142072 (0.573552) | 0.674299 / 4.805227 (-4.130929) | 0.136039 / 6.500664 (-6.364625) | 0.067629 / 0.075469 (-0.007840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333734 / 1.841788 (-0.508054) | 14.561943 / 8.074308 (6.487634) | 14.455385 / 10.191392 (4.263993) | 0.132020 / 0.680424 (-0.548404) | 0.016893 / 0.534201 (-0.517308) | 0.367146 / 0.579283 (-0.212137) | 0.399623 / 0.434364 (-0.034741) | 0.432658 / 0.540337 (-0.107680) | 0.530475 / 1.386936 (-0.856461) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006045 / 0.011353 (-0.005308) | 0.003906 / 0.011008 (-0.007103) | 0.097558 / 0.038508 (0.059050) | 0.038827 / 0.023109 (0.015718) | 0.393564 / 0.275898 (0.117666) | 0.442459 / 0.323480 (0.118980) | 0.004792 / 0.007986 (-0.003194) | 0.002984 / 0.004328 (-0.001345) | 0.076419 / 0.004250 (0.072169) | 0.053606 / 0.037052 (0.016554) | 0.409743 / 0.258489 (0.151254) | 0.445753 / 0.293841 (0.151912) | 0.027753 / 0.128546 (-0.100793) | 0.008428 / 0.075646 (-0.067219) | 0.310267 / 0.419271 (-0.109004) | 0.057582 / 0.043533 (0.014049) | 0.396624 / 0.255139 (0.141485) | 0.416288 / 0.283200 (0.133089) | 0.029048 / 0.141683 (-0.112635) | 1.495362 / 1.452155 (0.043207) | 1.546331 / 1.492716 (0.053615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203832 / 0.018006 (0.185826) | 0.423649 / 0.000490 (0.423160) | 0.004533 / 0.000200 (0.004333) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023084 / 0.037411 (-0.014328) | 0.100503 / 0.014526 (0.085977) | 0.105058 / 0.176557 (-0.071499) | 0.168506 / 0.737135 (-0.568629) | 0.112019 / 0.296338 (-0.184320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425877 / 0.215209 (0.210668) | 4.251278 / 2.077655 (2.173624) | 1.931339 / 1.504120 (0.427219) | 1.730578 / 1.541195 (0.189383) | 1.750637 / 1.468490 (0.282147) | 0.559307 / 4.584777 (-4.025470) | 3.461665 / 3.745712 (-0.284047) | 2.826959 / 5.269862 (-2.442903) | 1.418448 / 4.565676 (-3.147229) | 0.067881 / 0.424275 (-0.356394) | 0.011394 / 0.007607 (0.003787) | 0.533226 / 0.226044 (0.307181) | 5.341849 / 2.268929 (3.072921) | 2.367832 / 55.444624 (-53.076792) | 2.027240 / 6.876477 (-4.849236) | 2.095852 / 2.142072 (-0.046220) | 0.673790 / 4.805227 (-4.131437) | 0.136044 / 6.500664 (-6.364620) | 0.066350 / 0.075469 (-0.009119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203740 / 1.841788 (-0.638048) | 13.720879 / 8.074308 (5.646571) | 13.405939 / 10.191392 (3.214547) | 0.146792 / 0.680424 (-0.533632) | 0.016844 / 0.534201 (-0.517357) | 0.373455 / 0.579283 (-0.205828) | 0.394596 / 0.434364 (-0.039768) | 0.464715 / 0.540337 (-0.075623) | 0.558931 / 1.386936 (-0.828005) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006118 / 0.011353 (-0.005235) | 0.003817 / 0.011008 (-0.007191) | 0.077494 / 0.038508 (0.038985) | 0.037507 / 0.023109 (0.014398) | 0.387030 / 0.275898 (0.111132) | 0.437352 / 0.323480 (0.113872) | 0.004810 / 0.007986 (-0.003176) | 0.002935 / 0.004328 (-0.001394) | 0.077143 / 0.004250 (0.072892) | 0.053986 / 0.037052 (0.016933) | 0.393164 / 0.258489 (0.134675) | 0.449603 / 0.293841 (0.155762) | 0.029303 / 0.128546 (-0.099244) | 0.008481 / 0.075646 (-0.067165) | 0.083363 / 0.419271 (-0.335908) | 0.043877 / 0.043533 (0.000344) | 0.378175 / 0.255139 (0.123036) | 0.403996 / 0.283200 (0.120797) | 0.021688 / 0.141683 (-0.119995) | 1.541606 / 1.452155 (0.089452) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236759 / 0.018006 (0.218752) | 0.416221 / 0.000490 (0.415732) | 0.000862 / 0.000200 (0.000662) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025543 / 0.037411 (-0.011868) | 0.101731 / 0.014526 (0.087206) | 0.108482 / 0.176557 (-0.068075) | 0.160290 / 0.737135 (-0.576845) | 0.111392 / 0.296338 (-0.184946) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457767 / 0.215209 (0.242558) | 4.565976 / 2.077655 (2.488321) | 2.245413 / 1.504120 (0.741294) | 2.031458 / 1.541195 (0.490264) | 2.073193 / 1.468490 (0.604702) | 0.560461 / 4.584777 (-4.024316) | 3.422536 / 3.745712 (-0.323176) | 2.977017 / 5.269862 (-2.292845) | 1.377021 / 4.565676 (-3.188655) | 0.068444 / 0.424275 (-0.355831) | 0.011036 / 0.007607 (0.003429) | 0.571501 / 0.226044 (0.345456) | 5.702652 / 2.268929 (3.433723) | 2.727132 / 55.444624 (-52.717492) | 2.399269 / 6.876477 (-4.477208) | 2.574281 / 2.142072 (0.432208) | 0.682600 / 4.805227 (-4.122627) | 0.136943 / 6.500664 (-6.363722) | 0.067126 / 0.075469 (-0.008343) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322196 / 1.841788 (-0.519592) | 14.239509 / 8.074308 (6.165201) | 14.235779 / 10.191392 (4.044387) | 0.148262 / 0.680424 (-0.532162) | 0.016566 / 0.534201 (-0.517635) | 0.364034 / 0.579283 (-0.215249) | 0.399157 / 0.434364 (-0.035207) | 0.426348 / 0.540337 (-0.113990) | 0.520804 / 1.386936 (-0.866132) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007808 / 0.011353 (-0.003545) | 0.004706 / 0.011008 (-0.006303) | 0.100530 / 0.038508 (0.062022) | 0.052052 / 0.023109 (0.028943) | 0.419300 / 0.275898 (0.143402) | 0.488451 / 0.323480 (0.164971) | 0.006350 / 0.007986 (-0.001636) | 0.003875 / 0.004328 (-0.000453) | 0.076489 / 0.004250 (0.072238) | 0.077554 / 0.037052 (0.040502) | 0.435863 / 0.258489 (0.177373) | 0.483241 / 0.293841 (0.189400) | 0.037518 / 0.128546 (-0.091028) | 0.009857 / 0.075646 (-0.065789) | 0.340933 / 0.419271 (-0.078339) | 0.087046 / 0.043533 (0.043514) | 0.410721 / 0.255139 (0.155582) | 0.428995 / 0.283200 (0.145795) | 0.041701 / 0.141683 (-0.099982) | 1.821017 / 1.452155 (0.368862) | 1.837021 / 1.492716 (0.344305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228444 / 0.018006 (0.210438) | 0.480446 / 0.000490 (0.479956) | 0.004963 / 0.000200 (0.004763) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032485 / 0.037411 (-0.004926) | 0.096500 / 0.014526 (0.081974) | 0.111547 / 0.176557 (-0.065010) | 0.178842 / 0.737135 (-0.558294) | 0.111099 / 0.296338 (-0.185240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467159 / 0.215209 (0.251950) | 4.701676 / 2.077655 (2.624021) | 2.390560 / 1.504120 (0.886440) | 2.197722 / 1.541195 (0.656528) | 2.264705 / 1.468490 (0.796215) | 0.568667 / 4.584777 (-4.016110) | 4.200724 / 3.745712 (0.455012) | 3.777625 / 5.269862 (-1.492236) | 2.372451 / 4.565676 (-2.193225) | 0.067562 / 0.424275 (-0.356714) | 0.008947 / 0.007607 (0.001340) | 0.556910 / 0.226044 (0.330865) | 5.528927 / 2.268929 (3.259998) | 2.902780 / 55.444624 (-52.541844) | 2.507933 / 6.876477 (-4.368544) | 2.734627 / 2.142072 (0.592554) | 0.683305 / 4.805227 (-4.121922) | 0.158288 / 6.500664 (-6.342376) | 0.071252 / 0.075469 (-0.004217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.487502 / 1.841788 (-0.354286) | 22.193341 / 8.074308 (14.119033) | 15.922607 / 10.191392 (5.731215) | 0.172189 / 0.680424 (-0.508235) | 0.021502 / 0.534201 (-0.512699) | 0.471198 / 0.579283 (-0.108085) | 0.475979 / 0.434364 (0.041615) | 0.544675 / 0.540337 (0.004338) | 0.756102 / 1.386936 (-0.630834) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007635 / 0.011353 (-0.003717) | 0.004614 / 0.011008 (-0.006394) | 0.075852 / 0.038508 (0.037344) | 0.049700 / 0.023109 (0.026591) | 0.425957 / 0.275898 (0.150059) | 0.512590 / 0.323480 (0.189110) | 0.006921 / 0.007986 (-0.001065) | 0.003714 / 0.004328 (-0.000615) | 0.075536 / 0.004250 (0.071286) | 0.070206 / 0.037052 (0.033153) | 0.455706 / 0.258489 (0.197217) | 0.512231 / 0.293841 (0.218390) | 0.036685 / 0.128546 (-0.091861) | 0.009793 / 0.075646 (-0.065853) | 0.084208 / 0.419271 (-0.335064) | 0.065262 / 0.043533 (0.021729) | 0.423761 / 0.255139 (0.168622) | 0.456791 / 0.283200 (0.173591) | 0.044539 / 0.141683 (-0.097144) | 1.797029 / 1.452155 (0.344874) | 1.864124 / 1.492716 (0.371408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.366840 / 0.018006 (0.348834) | 0.479254 / 0.000490 (0.478765) | 0.070383 / 0.000200 (0.070183) | 0.000762 / 0.000054 (0.000707) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034233 / 0.037411 (-0.003178) | 0.103140 / 0.014526 (0.088614) | 0.117099 / 0.176557 (-0.059457) | 0.178532 / 0.737135 (-0.558603) | 0.120092 / 0.296338 (-0.176247) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492993 / 0.215209 (0.277784) | 4.878776 / 2.077655 (2.801121) | 2.566666 / 1.504120 (1.062547) | 2.356383 / 1.541195 (0.815188) | 2.454723 / 1.468490 (0.986233) | 0.571432 / 4.584777 (-4.013345) | 4.240554 / 3.745712 (0.494842) | 7.509259 / 5.269862 (2.239398) | 4.040294 / 4.565676 (-0.525382) | 0.067409 / 0.424275 (-0.356866) | 0.008657 / 0.007607 (0.001050) | 0.585751 / 0.226044 (0.359707) | 5.967668 / 2.268929 (3.698739) | 3.195573 / 55.444624 (-52.249052) | 2.839772 / 6.876477 (-4.036704) | 2.806319 / 2.142072 (0.664246) | 0.681502 / 4.805227 (-4.123725) | 0.158673 / 6.500664 (-6.341991) | 0.073224 / 0.075469 (-0.002245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623335 / 1.841788 (-0.218453) | 22.490806 / 8.074308 (14.416498) | 16.762435 / 10.191392 (6.571043) | 0.180961 / 0.680424 (-0.499463) | 0.022716 / 0.534201 (-0.511485) | 0.472910 / 0.579283 (-0.106373) | 0.471616 / 0.434364 (0.037252) | 0.548192 / 0.540337 (0.007854) | 0.734357 / 1.386936 (-0.652579) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005858 / 0.011353 (-0.005495) | 0.003512 / 0.011008 (-0.007497) | 0.079739 / 0.038508 (0.041231) | 0.057736 / 0.023109 (0.034627) | 0.317640 / 0.275898 (0.041742) | 0.354157 / 0.323480 (0.030677) | 0.004772 / 0.007986 (-0.003214) | 0.002824 / 0.004328 (-0.001504) | 0.063288 / 0.004250 (0.059037) | 0.049542 / 0.037052 (0.012489) | 0.323974 / 0.258489 (0.065485) | 0.372149 / 0.293841 (0.078308) | 0.026841 / 0.128546 (-0.101705) | 0.007846 / 0.075646 (-0.067800) | 0.262546 / 0.419271 (-0.156725) | 0.051952 / 0.043533 (0.008420) | 0.319439 / 0.255139 (0.064300) | 0.343862 / 0.283200 (0.060663) | 0.027021 / 0.141683 (-0.114662) | 1.445211 / 1.452155 (-0.006944) | 1.485006 / 1.492716 (-0.007711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183174 / 0.018006 (0.165167) | 0.422794 / 0.000490 (0.422304) | 0.004148 / 0.000200 (0.003948) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023037 / 0.037411 (-0.014374) | 0.071300 / 0.014526 (0.056775) | 0.083022 / 0.176557 (-0.093535) | 0.146215 / 0.737135 (-0.590920) | 0.082549 / 0.296338 (-0.213789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422846 / 0.215209 (0.207637) | 4.215280 / 2.077655 (2.137626) | 2.256802 / 1.504120 (0.752682) | 2.056867 / 1.541195 (0.515673) | 2.102478 / 1.468490 (0.633988) | 0.497552 / 4.584777 (-4.087225) | 3.049716 / 3.745712 (-0.695996) | 4.209227 / 5.269862 (-1.060635) | 2.599947 / 4.565676 (-1.965730) | 0.059131 / 0.424275 (-0.365144) | 0.006459 / 0.007607 (-0.001148) | 0.495047 / 0.226044 (0.269003) | 4.952332 / 2.268929 (2.683404) | 2.675260 / 55.444624 (-52.769365) | 2.333223 / 6.876477 (-4.543254) | 2.449573 / 2.142072 (0.307500) | 0.583420 / 4.805227 (-4.221807) | 0.125140 / 6.500664 (-6.375524) | 0.060209 / 0.075469 (-0.015260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215033 / 1.841788 (-0.626755) | 18.101107 / 8.074308 (10.026799) | 13.489222 / 10.191392 (3.297830) | 0.147122 / 0.680424 (-0.533302) | 0.016567 / 0.534201 (-0.517634) | 0.329909 / 0.579283 (-0.249374) | 0.340952 / 0.434364 (-0.093412) | 0.379166 / 0.540337 (-0.161172) | 0.510767 / 1.386936 (-0.876169) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005942 / 0.011353 (-0.005411) | 0.003628 / 0.011008 (-0.007380) | 0.061975 / 0.038508 (0.023467) | 0.058331 / 0.023109 (0.035221) | 0.393277 / 0.275898 (0.117379) | 0.410740 / 0.323480 (0.087261) | 0.004546 / 0.007986 (-0.003440) | 0.002826 / 0.004328 (-0.001503) | 0.062216 / 0.004250 (0.057966) | 0.049801 / 0.037052 (0.012748) | 0.394070 / 0.258489 (0.135581) | 0.414407 / 0.293841 (0.120566) | 0.027161 / 0.128546 (-0.101385) | 0.007901 / 0.075646 (-0.067746) | 0.066778 / 0.419271 (-0.352493) | 0.041354 / 0.043533 (-0.002179) | 0.379432 / 0.255139 (0.124293) | 0.402966 / 0.283200 (0.119766) | 0.020279 / 0.141683 (-0.121404) | 1.416986 / 1.452155 (-0.035169) | 1.474335 / 1.492716 (-0.018382) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226147 / 0.018006 (0.208140) | 0.404361 / 0.000490 (0.403871) | 0.000358 / 0.000200 (0.000158) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025105 / 0.037411 (-0.012306) | 0.075849 / 0.014526 (0.061323) | 0.084781 / 0.176557 (-0.091775) | 0.137415 / 0.737135 (-0.599720) | 0.086288 / 0.296338 (-0.210051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445925 / 0.215209 (0.230716) | 4.453478 / 2.077655 (2.375823) | 2.419048 / 1.504120 (0.914928) | 2.246363 / 1.541195 (0.705168) | 2.304022 / 1.468490 (0.835532) | 0.499132 / 4.584777 (-4.085645) | 3.001336 / 3.745712 (-0.744376) | 2.902593 / 5.269862 (-2.367269) | 1.819843 / 4.565676 (-2.745834) | 0.057210 / 0.424275 (-0.367065) | 0.006338 / 0.007607 (-0.001269) | 0.523280 / 0.226044 (0.297236) | 5.235969 / 2.268929 (2.967040) | 2.897585 / 55.444624 (-52.547039) | 2.541586 / 6.876477 (-4.334891) | 2.564233 / 2.142072 (0.422160) | 0.584714 / 4.805227 (-4.220513) | 0.124611 / 6.500664 (-6.376053) | 0.061774 / 0.075469 (-0.013695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.349799 / 1.841788 (-0.491988) | 18.225076 / 8.074308 (10.150768) | 13.781518 / 10.191392 (3.590126) | 0.130562 / 0.680424 (-0.549862) | 0.016434 / 0.534201 (-0.517767) | 0.331607 / 0.579283 (-0.247676) | 0.343456 / 0.434364 (-0.090908) | 0.380437 / 0.540337 (-0.159900) | 0.522793 / 1.386936 (-0.864143) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.013721 / 0.011353 (0.002368) | 0.005715 / 0.011008 (-0.005293) | 0.090116 / 0.038508 (0.051608) | 0.087185 / 0.023109 (0.064075) | 0.427813 / 0.275898 (0.151915) | 0.390614 / 0.323480 (0.067135) | 0.006976 / 0.007986 (-0.001009) | 0.004231 / 0.004328 (-0.000098) | 0.078320 / 0.004250 (0.074070) | 0.066235 / 0.037052 (0.029183) | 0.439904 / 0.258489 (0.181415) | 0.424119 / 0.293841 (0.130278) | 0.050362 / 0.128546 (-0.078184) | 0.014992 / 0.075646 (-0.060654) | 0.293519 / 0.419271 (-0.125753) | 0.066906 / 0.043533 (0.023373) | 0.449657 / 0.255139 (0.194518) | 0.393800 / 0.283200 (0.110600) | 0.032258 / 0.141683 (-0.109425) | 1.539534 / 1.452155 (0.087379) | 1.675292 / 1.492716 (0.182576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210515 / 0.018006 (0.192508) | 0.506817 / 0.000490 (0.506327) | 0.001938 / 0.000200 (0.001738) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026019 / 0.037411 (-0.011393) | 0.080635 / 0.014526 (0.066109) | 0.103050 / 0.176557 (-0.073507) | 0.160597 / 0.737135 (-0.576538) | 0.095844 / 0.296338 (-0.200495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506359 / 0.215209 (0.291150) | 5.041586 / 2.077655 (2.963931) | 2.198288 / 1.504120 (0.694168) | 1.987544 / 1.541195 (0.446349) | 1.866790 / 1.468490 (0.398300) | 0.681642 / 4.584777 (-3.903135) | 4.719306 / 3.745712 (0.973593) | 7.669869 / 5.269862 (2.400008) | 4.466082 / 4.565676 (-0.099595) | 0.092974 / 0.424275 (-0.331301) | 0.008196 / 0.007607 (0.000589) | 0.707656 / 0.226044 (0.481612) | 6.974507 / 2.268929 (4.705579) | 3.254206 / 55.444624 (-52.190418) | 2.499019 / 6.876477 (-4.377457) | 2.509089 / 2.142072 (0.367017) | 0.915952 / 4.805227 (-3.889276) | 0.192119 / 6.500664 (-6.308545) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309078 / 1.841788 (-0.532710) | 19.660348 / 8.074308 (11.586040) | 16.659582 / 10.191392 (6.468190) | 0.194315 / 0.680424 (-0.486109) | 0.027773 / 0.534201 (-0.506428) | 0.401241 / 0.579283 (-0.178042) | 0.515799 / 0.434364 (0.081435) | 0.488772 / 0.540337 (-0.051566) | 0.604790 / 1.386936 (-0.782146) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006823 / 0.011353 (-0.004530) | 0.003940 / 0.011008 (-0.007068) | 0.061533 / 0.038508 (0.023025) | 0.065241 / 0.023109 (0.042132) | 0.411790 / 0.275898 (0.135892) | 0.475720 / 0.323480 (0.152241) | 0.005376 / 0.007986 (-0.002609) | 0.003433 / 0.004328 (-0.000895) | 0.065703 / 0.004250 (0.061452) | 0.050736 / 0.037052 (0.013683) | 0.435890 / 0.258489 (0.177401) | 0.436698 / 0.293841 (0.142857) | 0.040357 / 0.128546 (-0.088189) | 0.011578 / 0.075646 (-0.064069) | 0.072831 / 0.419271 (-0.346440) | 0.055698 / 0.043533 (0.012165) | 0.408225 / 0.255139 (0.153086) | 0.439551 / 0.283200 (0.156352) | 0.030469 / 0.141683 (-0.111214) | 1.443866 / 1.452155 (-0.008289) | 1.502022 / 1.492716 (0.009306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290338 / 0.018006 (0.272332) | 0.540726 / 0.000490 (0.540236) | 0.003244 / 0.000200 (0.003044) | 0.000170 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030865 / 0.037411 (-0.006547) | 0.090866 / 0.014526 (0.076340) | 0.106224 / 0.176557 (-0.070332) | 0.166583 / 0.737135 (-0.570553) | 0.104448 / 0.296338 (-0.191891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518025 / 0.215209 (0.302816) | 6.027065 / 2.077655 (3.949410) | 2.671840 / 1.504120 (1.167720) | 2.273949 / 1.541195 (0.732754) | 2.414892 / 1.468490 (0.946402) | 0.774318 / 4.584777 (-3.810459) | 5.020364 / 3.745712 (1.274652) | 4.146927 / 5.269862 (-1.122934) | 2.584598 / 4.565676 (-1.981078) | 0.089519 / 0.424275 (-0.334756) | 0.009181 / 0.007607 (0.001574) | 0.654467 / 0.226044 (0.428423) | 6.421595 / 2.268929 (4.152666) | 3.091589 / 55.444624 (-52.353036) | 2.554798 / 6.876477 (-4.321679) | 2.441354 / 2.142072 (0.299282) | 0.943386 / 4.805227 (-3.861841) | 0.173641 / 6.500664 (-6.327023) | 0.072209 / 0.075469 (-0.003260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.557147 / 1.841788 (-0.284641) | 19.980747 / 8.074308 (11.906439) | 17.816813 / 10.191392 (7.625421) | 0.212078 / 0.680424 (-0.468346) | 0.025435 / 0.534201 (-0.508766) | 0.396200 / 0.579283 (-0.183084) | 0.546249 / 0.434364 (0.111885) | 0.459632 / 0.540337 (-0.080705) | 0.616548 / 1.386936 (-0.770388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006897 / 0.011353 (-0.004456) | 0.004207 / 0.011008 (-0.006802) | 0.104828 / 0.038508 (0.066320) | 0.048054 / 0.023109 (0.024945) | 0.373991 / 0.275898 (0.098093) | 0.426740 / 0.323480 (0.103260) | 0.005540 / 0.007986 (-0.002446) | 0.003531 / 0.004328 (-0.000797) | 0.079304 / 0.004250 (0.075053) | 0.066996 / 0.037052 (0.029944) | 0.370675 / 0.258489 (0.112186) | 0.414154 / 0.293841 (0.120313) | 0.031567 / 0.128546 (-0.096979) | 0.008843 / 0.075646 (-0.066803) | 0.357426 / 0.419271 (-0.061845) | 0.067040 / 0.043533 (0.023508) | 0.362384 / 0.255139 (0.107245) | 0.376056 / 0.283200 (0.092856) | 0.032985 / 0.141683 (-0.108697) | 1.560603 / 1.452155 (0.108448) | 1.619024 / 1.492716 (0.126308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229059 / 0.018006 (0.211053) | 0.440513 / 0.000490 (0.440023) | 0.004647 / 0.000200 (0.004447) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029517 / 0.037411 (-0.007894) | 0.120974 / 0.014526 (0.106448) | 0.125070 / 0.176557 (-0.051486) | 0.184695 / 0.737135 (-0.552441) | 0.130244 / 0.296338 (-0.166095) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436930 / 0.215209 (0.221721) | 4.356118 / 2.077655 (2.278463) | 2.049169 / 1.504120 (0.545049) | 1.842898 / 1.541195 (0.301703) | 1.918948 / 1.468490 (0.450458) | 0.553573 / 4.584777 (-4.031204) | 3.883195 / 3.745712 (0.137483) | 3.209780 / 5.269862 (-2.060081) | 1.551707 / 4.565676 (-3.013970) | 0.068181 / 0.424275 (-0.356094) | 0.012370 / 0.007607 (0.004762) | 0.539899 / 0.226044 (0.313854) | 5.380008 / 2.268929 (3.111079) | 2.518178 / 55.444624 (-52.926446) | 2.174190 / 6.876477 (-4.702286) | 2.317812 / 2.142072 (0.175740) | 0.674154 / 4.805227 (-4.131073) | 0.149313 / 6.500664 (-6.351351) | 0.068297 / 0.075469 (-0.007172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261426 / 1.841788 (-0.580362) | 15.316378 / 8.074308 (7.242070) | 13.573512 / 10.191392 (3.382120) | 0.190022 / 0.680424 (-0.490401) | 0.018697 / 0.534201 (-0.515504) | 0.448122 / 0.579283 (-0.131161) | 0.435044 / 0.434364 (0.000681) | 0.550065 / 0.540337 (0.009728) | 0.653547 / 1.386936 (-0.733389) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007116 / 0.011353 (-0.004237) | 0.004375 / 0.011008 (-0.006633) | 0.081793 / 0.038508 (0.043285) | 0.047980 / 0.023109 (0.024871) | 0.392185 / 0.275898 (0.116287) | 0.462263 / 0.323480 (0.138783) | 0.005574 / 0.007986 (-0.002412) | 0.003552 / 0.004328 (-0.000776) | 0.080413 / 0.004250 (0.076162) | 0.065539 / 0.037052 (0.028487) | 0.413137 / 0.258489 (0.154648) | 0.467377 / 0.293841 (0.173536) | 0.034386 / 0.128546 (-0.094160) | 0.009183 / 0.075646 (-0.066464) | 0.087542 / 0.419271 (-0.331730) | 0.053954 / 0.043533 (0.010421) | 0.385096 / 0.255139 (0.129957) | 0.404900 / 0.283200 (0.121701) | 0.025908 / 0.141683 (-0.115775) | 1.550159 / 1.452155 (0.098005) | 1.598794 / 1.492716 (0.106078) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246222 / 0.018006 (0.228216) | 0.441095 / 0.000490 (0.440605) | 0.006863 / 0.000200 (0.006663) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032179 / 0.037411 (-0.005233) | 0.120112 / 0.014526 (0.105586) | 0.129326 / 0.176557 (-0.047230) | 0.184542 / 0.737135 (-0.552593) | 0.135038 / 0.296338 (-0.161300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459002 / 0.215209 (0.243793) | 4.580258 / 2.077655 (2.502604) | 2.296689 / 1.504120 (0.792569) | 2.104338 / 1.541195 (0.563143) | 2.182896 / 1.468490 (0.714406) | 0.546447 / 4.584777 (-4.038330) | 3.854047 / 3.745712 (0.108335) | 1.873829 / 5.269862 (-3.396032) | 1.116484 / 4.565676 (-3.449193) | 0.067158 / 0.424275 (-0.357117) | 0.012035 / 0.007607 (0.004428) | 0.556642 / 0.226044 (0.330597) | 5.574436 / 2.268929 (3.305508) | 2.828223 / 55.444624 (-52.616402) | 2.519851 / 6.876477 (-4.356626) | 2.668594 / 2.142072 (0.526521) | 0.675989 / 4.805227 (-4.129238) | 0.146075 / 6.500664 (-6.354589) | 0.067788 / 0.075469 (-0.007681) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345958 / 1.841788 (-0.495830) | 15.672748 / 8.074308 (7.598440) | 14.937583 / 10.191392 (4.746191) | 0.163479 / 0.680424 (-0.516945) | 0.018364 / 0.534201 (-0.515837) | 0.433296 / 0.579283 (-0.145987) | 0.432463 / 0.434364 (-0.001901) | 0.512000 / 0.540337 (-0.028338) | 0.619397 / 1.386936 (-0.767539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010097 / 0.011353 (-0.001256) | 0.005070 / 0.011008 (-0.005939) | 0.118638 / 0.038508 (0.080130) | 0.043651 / 0.023109 (0.020542) | 0.356074 / 0.275898 (0.080176) | 0.414578 / 0.323480 (0.091098) | 0.005939 / 0.007986 (-0.002046) | 0.004927 / 0.004328 (0.000598) | 0.089545 / 0.004250 (0.085294) | 0.067533 / 0.037052 (0.030481) | 0.371550 / 0.258489 (0.113061) | 0.417808 / 0.293841 (0.123967) | 0.045186 / 0.128546 (-0.083361) | 0.015763 / 0.075646 (-0.059883) | 0.393304 / 0.419271 (-0.025967) | 0.065123 / 0.043533 (0.021591) | 0.345057 / 0.255139 (0.089918) | 0.378809 / 0.283200 (0.095610) | 0.033243 / 0.141683 (-0.108440) | 1.679956 / 1.452155 (0.227802) | 1.775456 / 1.492716 (0.282739) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229723 / 0.018006 (0.211717) | 0.554630 / 0.000490 (0.554140) | 0.008729 / 0.000200 (0.008529) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027284 / 0.037411 (-0.010128) | 0.114741 / 0.014526 (0.100215) | 0.129188 / 0.176557 (-0.047369) | 0.189270 / 0.737135 (-0.547866) | 0.126000 / 0.296338 (-0.170339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580417 / 0.215209 (0.365208) | 5.829337 / 2.077655 (3.751683) | 2.421191 / 1.504120 (0.917071) | 2.063673 / 1.541195 (0.522479) | 2.133427 / 1.468490 (0.664937) | 0.830964 / 4.584777 (-3.753813) | 5.107139 / 3.745712 (1.361427) | 4.599451 / 5.269862 (-0.670410) | 2.406502 / 4.565676 (-2.159175) | 0.100422 / 0.424275 (-0.323853) | 0.011850 / 0.007607 (0.004243) | 0.741881 / 0.226044 (0.515836) | 7.425689 / 2.268929 (5.156760) | 3.068948 / 55.444624 (-52.375676) | 2.496292 / 6.876477 (-4.380184) | 2.566420 / 2.142072 (0.424348) | 1.093084 / 4.805227 (-3.712144) | 0.224106 / 6.500664 (-6.276558) | 0.084549 / 0.075469 (0.009080) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.416315 / 1.841788 (-0.425473) | 16.306901 / 8.074308 (8.232593) | 19.792419 / 10.191392 (9.601027) | 0.224223 / 0.680424 (-0.456201) | 0.026385 / 0.534201 (-0.507816) | 0.463460 / 0.579283 (-0.115823) | 0.598385 / 0.434364 (0.164021) | 0.543981 / 0.540337 (0.003644) | 0.647454 / 1.386936 (-0.739482) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009470 / 0.011353 (-0.001883) | 0.004800 / 0.011008 (-0.006208) | 0.094276 / 0.038508 (0.055768) | 0.045157 / 0.023109 (0.022048) | 0.397302 / 0.275898 (0.121404) | 0.474213 / 0.323480 (0.150733) | 0.005826 / 0.007986 (-0.002160) | 0.003724 / 0.004328 (-0.000605) | 0.090060 / 0.004250 (0.085809) | 0.066671 / 0.037052 (0.029618) | 0.439560 / 0.258489 (0.181071) | 0.468598 / 0.293841 (0.174757) | 0.044549 / 0.128546 (-0.083997) | 0.014000 / 0.075646 (-0.061646) | 0.110457 / 0.419271 (-0.308815) | 0.065898 / 0.043533 (0.022365) | 0.408101 / 0.255139 (0.152962) | 0.433473 / 0.283200 (0.150273) | 0.038438 / 0.141683 (-0.103245) | 1.767781 / 1.452155 (0.315626) | 1.791575 / 1.492716 (0.298859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230257 / 0.018006 (0.212251) | 0.492280 / 0.000490 (0.491790) | 0.005110 / 0.000200 (0.004910) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028854 / 0.037411 (-0.008557) | 0.111702 / 0.014526 (0.097176) | 0.122040 / 0.176557 (-0.054517) | 0.179103 / 0.737135 (-0.558032) | 0.128869 / 0.296338 (-0.167470) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634795 / 0.215209 (0.419586) | 6.204760 / 2.077655 (4.127105) | 2.692479 / 1.504120 (1.188359) | 2.324260 / 1.541195 (0.783066) | 2.380640 / 1.468490 (0.912149) | 0.887827 / 4.584777 (-3.696950) | 5.251648 / 3.745712 (1.505935) | 2.632767 / 5.269862 (-2.637095) | 1.745721 / 4.565676 (-2.819955) | 0.108364 / 0.424275 (-0.315911) | 0.013409 / 0.007607 (0.005802) | 0.783427 / 0.226044 (0.557383) | 7.765144 / 2.268929 (5.496216) | 3.340686 / 55.444624 (-52.103938) | 2.715340 / 6.876477 (-4.161137) | 2.768604 / 2.142072 (0.626531) | 1.119746 / 4.805227 (-3.685481) | 0.210804 / 6.500664 (-6.289860) | 0.072600 / 0.075469 (-0.002869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517334 / 1.841788 (-0.324454) | 17.046837 / 8.074308 (8.972529) | 19.371090 / 10.191392 (9.179698) | 0.194275 / 0.680424 (-0.486148) | 0.026712 / 0.534201 (-0.507488) | 0.462731 / 0.579283 (-0.116552) | 0.568958 / 0.434364 (0.134595) | 0.555707 / 0.540337 (0.015370) | 0.663654 / 1.386936 (-0.723283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d20476b1d4c8e11e0ffafc1570cbf4bd19011cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006423 / 0.011353 (-0.004930) | 0.003882 / 0.011008 (-0.007126) | 0.082976 / 0.038508 (0.044468) | 0.071281 / 0.023109 (0.048171) | 0.311367 / 0.275898 (0.035469) | 0.348228 / 0.323480 (0.024748) | 0.005315 / 0.007986 (-0.002671) | 0.003326 / 0.004328 (-0.001003) | 0.064641 / 0.004250 (0.060391) | 0.056134 / 0.037052 (0.019081) | 0.314071 / 0.258489 (0.055582) | 0.360534 / 0.293841 (0.066693) | 0.030642 / 0.128546 (-0.097904) | 0.008301 / 0.075646 (-0.067345) | 0.285820 / 0.419271 (-0.133451) | 0.069241 / 0.043533 (0.025708) | 0.313995 / 0.255139 (0.058856) | 0.336656 / 0.283200 (0.053457) | 0.031686 / 0.141683 (-0.109997) | 1.467627 / 1.452155 (0.015472) | 1.536493 / 1.492716 (0.043777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196518 / 0.018006 (0.178512) | 0.458235 / 0.000490 (0.457745) | 0.005599 / 0.000200 (0.005399) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027371 / 0.037411 (-0.010040) | 0.080986 / 0.014526 (0.066460) | 0.093296 / 0.176557 (-0.083260) | 0.150592 / 0.737135 (-0.586543) | 0.094150 / 0.296338 (-0.202188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379412 / 0.215209 (0.164202) | 3.797927 / 2.077655 (1.720272) | 1.830654 / 1.504120 (0.326534) | 1.669569 / 1.541195 (0.128374) | 1.746738 / 1.468490 (0.278248) | 0.479536 / 4.584777 (-4.105241) | 3.592867 / 3.745712 (-0.152845) | 5.468098 / 5.269862 (0.198237) | 3.268013 / 4.565676 (-1.297663) | 0.056635 / 0.424275 (-0.367640) | 0.007224 / 0.007607 (-0.000383) | 0.456681 / 0.226044 (0.230636) | 4.566736 / 2.268929 (2.297807) | 2.362831 / 55.444624 (-53.081793) | 1.965141 / 6.876477 (-4.911336) | 2.156905 / 2.142072 (0.014833) | 0.572543 / 4.805227 (-4.232684) | 0.132203 / 6.500664 (-6.368461) | 0.059254 / 0.075469 (-0.016215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256134 / 1.841788 (-0.585654) | 19.905438 / 8.074308 (11.831130) | 14.179556 / 10.191392 (3.988164) | 0.168043 / 0.680424 (-0.512381) | 0.018215 / 0.534201 (-0.515986) | 0.392740 / 0.579283 (-0.186543) | 0.398397 / 0.434364 (-0.035967) | 0.463806 / 0.540337 (-0.076531) | 0.616248 / 1.386936 (-0.770688) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006564 / 0.011353 (-0.004789) | 0.003923 / 0.011008 (-0.007085) | 0.063929 / 0.038508 (0.025421) | 0.073780 / 0.023109 (0.050671) | 0.360242 / 0.275898 (0.084344) | 0.395078 / 0.323480 (0.071598) | 0.005265 / 0.007986 (-0.002720) | 0.003229 / 0.004328 (-0.001100) | 0.064094 / 0.004250 (0.059843) | 0.057468 / 0.037052 (0.020416) | 0.369530 / 0.258489 (0.111041) | 0.411159 / 0.293841 (0.117318) | 0.031278 / 0.128546 (-0.097268) | 0.008424 / 0.075646 (-0.067222) | 0.070411 / 0.419271 (-0.348860) | 0.048714 / 0.043533 (0.005181) | 0.361280 / 0.255139 (0.106141) | 0.382468 / 0.283200 (0.099269) | 0.023059 / 0.141683 (-0.118624) | 1.452369 / 1.452155 (0.000215) | 1.519192 / 1.492716 (0.026475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223745 / 0.018006 (0.205739) | 0.442086 / 0.000490 (0.441596) | 0.000379 / 0.000200 (0.000179) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030919 / 0.037411 (-0.006493) | 0.088483 / 0.014526 (0.073958) | 0.101165 / 0.176557 (-0.075391) | 0.154332 / 0.737135 (-0.582804) | 0.103030 / 0.296338 (-0.193309) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414520 / 0.215209 (0.199311) | 4.126754 / 2.077655 (2.049099) | 2.142677 / 1.504120 (0.638557) | 1.995300 / 1.541195 (0.454106) | 2.101678 / 1.468490 (0.633188) | 0.481099 / 4.584777 (-4.103678) | 3.562813 / 3.745712 (-0.182900) | 3.392463 / 5.269862 (-1.877399) | 1.983943 / 4.565676 (-2.581734) | 0.056594 / 0.424275 (-0.367681) | 0.007216 / 0.007607 (-0.000391) | 0.495085 / 0.226044 (0.269041) | 4.955640 / 2.268929 (2.686712) | 2.629434 / 55.444624 (-52.815191) | 2.269577 / 6.876477 (-4.606900) | 2.357708 / 2.142072 (0.215635) | 0.612370 / 4.805227 (-4.192857) | 0.131169 / 6.500664 (-6.369495) | 0.061029 / 0.075469 (-0.014440) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.339438 / 1.841788 (-0.502350) | 19.757611 / 8.074308 (11.683303) | 14.246254 / 10.191392 (4.054862) | 0.170750 / 0.680424 (-0.509674) | 0.018192 / 0.534201 (-0.516009) | 0.395693 / 0.579283 (-0.183590) | 0.411003 / 0.434364 (-0.023361) | 0.478531 / 0.540337 (-0.061806) | 0.650291 / 1.386936 (-0.736645) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e34d06d746688dd5d26e4c85517b7e1a2f361ca \"CML watermark\")\n" ]

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[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006038 / 0.011353 (-0.005315) | 0.003797 / 0.011008 (-0.007211) | 0.097686 / 0.038508 (0.059178) | 0.035235 / 0.023109 (0.012126) | 0.317294 / 0.275898 (0.041396) | 0.377682 / 0.323480 (0.054202) | 0.003485 / 0.007986 (-0.004501) | 0.003603 / 0.004328 (-0.000725) | 0.077268 / 0.004250 (0.073017) | 0.054649 / 0.037052 (0.017597) | 0.322293 / 0.258489 (0.063804) | 0.372277 / 0.293841 (0.078436) | 0.027927 / 0.128546 (-0.100619) | 0.008495 / 0.075646 (-0.067151) | 0.313078 / 0.419271 (-0.106193) | 0.046974 / 0.043533 (0.003441) | 0.313848 / 0.255139 (0.058709) | 0.338454 / 0.283200 (0.055255) | 0.020462 / 0.141683 (-0.121221) | 1.473027 / 1.452155 (0.020873) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221429 / 0.018006 (0.203423) | 0.412044 / 0.000490 (0.411555) | 0.005866 / 0.000200 (0.005666) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022870 / 0.037411 (-0.014541) | 0.099129 / 0.014526 (0.084603) | 0.103463 / 0.176557 (-0.073094) | 0.164969 / 0.737135 (-0.572166) | 0.110000 / 0.296338 (-0.186339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431311 / 0.215209 (0.216102) | 4.293562 / 2.077655 (2.215907) | 1.961209 / 1.504120 (0.457089) | 1.733680 / 1.541195 (0.192485) | 1.793171 / 1.468490 (0.324681) | 0.568566 / 4.584777 (-4.016211) | 3.401794 / 3.745712 (-0.343918) | 1.827949 / 5.269862 (-3.441913) | 1.055963 / 4.565676 (-3.509714) | 0.068459 / 0.424275 (-0.355816) | 0.011586 / 0.007607 (0.003979) | 0.533936 / 0.226044 (0.307891) | 5.347637 / 2.268929 (3.078708) | 2.378056 / 55.444624 (-53.066569) | 2.032159 / 6.876477 (-4.844318) | 2.159064 / 2.142072 (0.016991) | 0.674528 / 4.805227 (-4.130699) | 0.136859 / 6.500664 (-6.363805) | 0.066629 / 0.075469 (-0.008840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218084 / 1.841788 (-0.623704) | 14.141710 / 8.074308 (6.067402) | 13.588415 / 10.191392 (3.397023) | 0.155104 / 0.680424 (-0.525320) | 0.017160 / 0.534201 (-0.517041) | 0.375558 / 0.579283 (-0.203725) | 0.386293 / 0.434364 (-0.048071) | 0.459476 / 0.540337 (-0.080862) | 0.548561 / 1.386936 (-0.838375) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005878 / 0.011353 (-0.005475) | 0.003750 / 0.011008 (-0.007259) | 0.077720 / 0.038508 (0.039212) | 0.034955 / 0.023109 (0.011846) | 0.357480 / 0.275898 (0.081582) | 0.418210 / 0.323480 (0.094730) | 0.004566 / 0.007986 (-0.003419) | 0.002918 / 0.004328 (-0.001410) | 0.076517 / 0.004250 (0.072266) | 0.050202 / 0.037052 (0.013150) | 0.368166 / 0.258489 (0.109677) | 0.415681 / 0.293841 (0.121840) | 0.029496 / 0.128546 (-0.099050) | 0.008547 / 0.075646 (-0.067099) | 0.083037 / 0.419271 (-0.336234) | 0.045001 / 0.043533 (0.001468) | 0.356503 / 0.255139 (0.101364) | 0.383747 / 0.283200 (0.100547) | 0.025071 / 0.141683 (-0.116612) | 1.541985 / 1.452155 (0.089830) | 1.594710 / 1.492716 (0.101994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204491 / 0.018006 (0.186484) | 0.408686 / 0.000490 (0.408196) | 0.002505 / 0.000200 (0.002305) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024446 / 0.037411 (-0.012965) | 0.101432 / 0.014526 (0.086906) | 0.108105 / 0.176557 (-0.068452) | 0.161195 / 0.737135 (-0.575940) | 0.112671 / 0.296338 (-0.183667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459697 / 0.215209 (0.244488) | 4.570071 / 2.077655 (2.492416) | 2.211547 / 1.504120 (0.707427) | 1.996651 / 1.541195 (0.455457) | 2.015621 / 1.468490 (0.547131) | 0.567423 / 4.584777 (-4.017354) | 3.408027 / 3.745712 (-0.337685) | 2.913824 / 5.269862 (-2.356038) | 1.423223 / 4.565676 (-3.142453) | 0.068740 / 0.424275 (-0.355535) | 0.010997 / 0.007607 (0.003390) | 0.567340 / 0.226044 (0.341296) | 5.666280 / 2.268929 (3.397351) | 2.804934 / 55.444624 (-52.639690) | 2.430761 / 6.876477 (-4.445716) | 2.451820 / 2.142072 (0.309748) | 0.681926 / 4.805227 (-4.123301) | 0.137761 / 6.500664 (-6.362903) | 0.067173 / 0.075469 (-0.008296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329853 / 1.841788 (-0.511934) | 14.436232 / 8.074308 (6.361924) | 14.398645 / 10.191392 (4.207253) | 0.147421 / 0.680424 (-0.533002) | 0.016743 / 0.534201 (-0.517458) | 0.364964 / 0.579283 (-0.214319) | 0.387072 / 0.434364 (-0.047292) | 0.423892 / 0.540337 (-0.116445) | 0.521304 / 1.386936 (-0.865632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006463 / 0.011353 (-0.004889) | 0.003923 / 0.011008 (-0.007086) | 0.102096 / 0.038508 (0.063588) | 0.040230 / 0.023109 (0.017121) | 0.384688 / 0.275898 (0.108789) | 0.445574 / 0.323480 (0.122094) | 0.003590 / 0.007986 (-0.004395) | 0.004023 / 0.004328 (-0.000306) | 0.080125 / 0.004250 (0.075875) | 0.057406 / 0.037052 (0.020354) | 0.395049 / 0.258489 (0.136560) | 0.438065 / 0.293841 (0.144224) | 0.028963 / 0.128546 (-0.099583) | 0.008693 / 0.075646 (-0.066954) | 0.317158 / 0.419271 (-0.102114) | 0.047930 / 0.043533 (0.004397) | 0.382442 / 0.255139 (0.127303) | 0.410665 / 0.283200 (0.127466) | 0.020127 / 0.141683 (-0.121555) | 1.558554 / 1.452155 (0.106400) | 1.590959 / 1.492716 (0.098242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208826 / 0.018006 (0.190820) | 0.432037 / 0.000490 (0.431547) | 0.006509 / 0.000200 (0.006309) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023460 / 0.037411 (-0.013951) | 0.099070 / 0.014526 (0.084545) | 0.105771 / 0.176557 (-0.070785) | 0.166683 / 0.737135 (-0.570452) | 0.108755 / 0.296338 (-0.187583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424324 / 0.215209 (0.209115) | 4.225696 / 2.077655 (2.148042) | 1.910955 / 1.504120 (0.406835) | 1.704493 / 1.541195 (0.163298) | 1.782784 / 1.468490 (0.314293) | 0.562927 / 4.584777 (-4.021850) | 3.380163 / 3.745712 (-0.365550) | 1.779641 / 5.269862 (-3.490221) | 1.029134 / 4.565676 (-3.536543) | 0.068325 / 0.424275 (-0.355950) | 0.011528 / 0.007607 (0.003921) | 0.530141 / 0.226044 (0.304097) | 5.323443 / 2.268929 (3.054514) | 2.346956 / 55.444624 (-53.097668) | 2.013335 / 6.876477 (-4.863142) | 2.118531 / 2.142072 (-0.023541) | 0.675206 / 4.805227 (-4.130021) | 0.135473 / 6.500664 (-6.365191) | 0.064804 / 0.075469 (-0.010665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240179 / 1.841788 (-0.601608) | 14.692449 / 8.074308 (6.618141) | 13.672223 / 10.191392 (3.480831) | 0.147748 / 0.680424 (-0.532676) | 0.017119 / 0.534201 (-0.517082) | 0.369481 / 0.579283 (-0.209802) | 0.390133 / 0.434364 (-0.044231) | 0.458768 / 0.540337 (-0.081569) | 0.548989 / 1.386936 (-0.837947) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006319 / 0.011353 (-0.005034) | 0.003975 / 0.011008 (-0.007033) | 0.077886 / 0.038508 (0.039378) | 0.038322 / 0.023109 (0.015213) | 0.379851 / 0.275898 (0.103953) | 0.456749 / 0.323480 (0.133269) | 0.005320 / 0.007986 (-0.002665) | 0.003135 / 0.004328 (-0.001194) | 0.078272 / 0.004250 (0.074022) | 0.059919 / 0.037052 (0.022866) | 0.430062 / 0.258489 (0.171573) | 0.477432 / 0.293841 (0.183591) | 0.029713 / 0.128546 (-0.098833) | 0.008704 / 0.075646 (-0.066942) | 0.082488 / 0.419271 (-0.336784) | 0.044667 / 0.043533 (0.001134) | 0.354910 / 0.255139 (0.099771) | 0.434637 / 0.283200 (0.151438) | 0.026402 / 0.141683 (-0.115281) | 1.528825 / 1.452155 (0.076671) | 1.548209 / 1.492716 (0.055493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237988 / 0.018006 (0.219982) | 0.420402 / 0.000490 (0.419913) | 0.003098 / 0.000200 (0.002898) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011159) | 0.106137 / 0.014526 (0.091611) | 0.110273 / 0.176557 (-0.066284) | 0.165316 / 0.737135 (-0.571819) | 0.115720 / 0.296338 (-0.180619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454244 / 0.215209 (0.239035) | 4.526018 / 2.077655 (2.448364) | 2.395985 / 1.504120 (0.891865) | 2.234822 / 1.541195 (0.693627) | 2.370235 / 1.468490 (0.901745) | 0.567607 / 4.584777 (-4.017169) | 3.650156 / 3.745712 (-0.095556) | 3.360094 / 5.269862 (-1.909768) | 1.415252 / 4.565676 (-3.150424) | 0.068012 / 0.424275 (-0.356263) | 0.011135 / 0.007607 (0.003528) | 0.561967 / 0.226044 (0.335923) | 5.621819 / 2.268929 (3.352890) | 2.676912 / 55.444624 (-52.767712) | 2.338306 / 6.876477 (-4.538171) | 2.430888 / 2.142072 (0.288815) | 0.684576 / 4.805227 (-4.120651) | 0.138923 / 6.500664 (-6.361741) | 0.069933 / 0.075469 (-0.005536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313383 / 1.841788 (-0.528405) | 15.125088 / 8.074308 (7.050780) | 14.801501 / 10.191392 (4.610109) | 0.134235 / 0.680424 (-0.546189) | 0.017058 / 0.534201 (-0.517143) | 0.365166 / 0.579283 (-0.214117) | 0.395415 / 0.434364 (-0.038949) | 0.419355 / 0.540337 (-0.120983) | 0.513411 / 1.386936 (-0.873525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006722 / 0.011353 (-0.004631) | 0.004425 / 0.011008 (-0.006583) | 0.100850 / 0.038508 (0.062341) | 0.040816 / 0.023109 (0.017707) | 0.348823 / 0.275898 (0.072925) | 0.446285 / 0.323480 (0.122805) | 0.005738 / 0.007986 (-0.002247) | 0.003517 / 0.004328 (-0.000811) | 0.078824 / 0.004250 (0.074574) | 0.064695 / 0.037052 (0.027643) | 0.389894 / 0.258489 (0.131405) | 0.416107 / 0.293841 (0.122266) | 0.028850 / 0.128546 (-0.099696) | 0.009011 / 0.075646 (-0.066635) | 0.323117 / 0.419271 (-0.096154) | 0.049162 / 0.043533 (0.005629) | 0.340144 / 0.255139 (0.085005) | 0.382072 / 0.283200 (0.098872) | 0.023160 / 0.141683 (-0.118523) | 1.549218 / 1.452155 (0.097063) | 1.581266 / 1.492716 (0.088550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293360 / 0.018006 (0.275353) | 0.602189 / 0.000490 (0.601700) | 0.004608 / 0.000200 (0.004408) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.107088 / 0.014526 (0.092562) | 0.112188 / 0.176557 (-0.064369) | 0.174669 / 0.737135 (-0.562466) | 0.116359 / 0.296338 (-0.179980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422911 / 0.215209 (0.207702) | 4.231524 / 2.077655 (2.153869) | 1.906711 / 1.504120 (0.402591) | 1.706841 / 1.541195 (0.165646) | 1.792066 / 1.468490 (0.323576) | 0.559221 / 4.584777 (-4.025556) | 3.434280 / 3.745712 (-0.311433) | 1.918714 / 5.269862 (-3.351148) | 1.073070 / 4.565676 (-3.492606) | 0.067891 / 0.424275 (-0.356384) | 0.011927 / 0.007607 (0.004320) | 0.530843 / 0.226044 (0.304799) | 5.309213 / 2.268929 (3.040285) | 2.439246 / 55.444624 (-53.005378) | 2.101245 / 6.876477 (-4.775231) | 2.177436 / 2.142072 (0.035363) | 0.672150 / 4.805227 (-4.133077) | 0.137571 / 6.500664 (-6.363093) | 0.068343 / 0.075469 (-0.007126) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265262 / 1.841788 (-0.576525) | 14.988021 / 8.074308 (6.913713) | 13.611677 / 10.191392 (3.420285) | 0.171389 / 0.680424 (-0.509035) | 0.017681 / 0.534201 (-0.516520) | 0.377542 / 0.579283 (-0.201741) | 0.399475 / 0.434364 (-0.034889) | 0.469553 / 0.540337 (-0.070785) | 0.561888 / 1.386936 (-0.825048) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006782 / 0.011353 (-0.004571) | 0.004412 / 0.011008 (-0.006597) | 0.078594 / 0.038508 (0.040086) | 0.039930 / 0.023109 (0.016820) | 0.371879 / 0.275898 (0.095981) | 0.444910 / 0.323480 (0.121430) | 0.005707 / 0.007986 (-0.002279) | 0.003901 / 0.004328 (-0.000427) | 0.080125 / 0.004250 (0.075875) | 0.063977 / 0.037052 (0.026925) | 0.382781 / 0.258489 (0.124292) | 0.441791 / 0.293841 (0.147950) | 0.030428 / 0.128546 (-0.098118) | 0.009008 / 0.075646 (-0.066638) | 0.084447 / 0.419271 (-0.334824) | 0.044432 / 0.043533 (0.000899) | 0.365686 / 0.255139 (0.110547) | 0.394312 / 0.283200 (0.111113) | 0.024508 / 0.141683 (-0.117175) | 1.577020 / 1.452155 (0.124865) | 1.630259 / 1.492716 (0.137543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307960 / 0.018006 (0.289953) | 0.591473 / 0.000490 (0.590983) | 0.008098 / 0.000200 (0.007898) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029567 / 0.037411 (-0.007845) | 0.112773 / 0.014526 (0.098247) | 0.117362 / 0.176557 (-0.059194) | 0.174293 / 0.737135 (-0.562843) | 0.123156 / 0.296338 (-0.173182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457475 / 0.215209 (0.242266) | 4.599067 / 2.077655 (2.521412) | 2.262638 / 1.504120 (0.758518) | 2.124943 / 1.541195 (0.583748) | 2.339912 / 1.468490 (0.871422) | 0.566264 / 4.584777 (-4.018513) | 3.489261 / 3.745712 (-0.256451) | 1.925151 / 5.269862 (-3.344711) | 1.099389 / 4.565676 (-3.466287) | 0.068232 / 0.424275 (-0.356043) | 0.011660 / 0.007607 (0.004052) | 0.571227 / 0.226044 (0.345183) | 5.702059 / 2.268929 (3.433130) | 2.837701 / 55.444624 (-52.606924) | 2.605468 / 6.876477 (-4.271008) | 2.818396 / 2.142072 (0.676323) | 0.681856 / 4.805227 (-4.123371) | 0.141401 / 6.500664 (-6.359263) | 0.069728 / 0.075469 (-0.005741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354935 / 1.841788 (-0.486853) | 15.437404 / 8.074308 (7.363095) | 15.415193 / 10.191392 (5.223801) | 0.153459 / 0.680424 (-0.526964) | 0.017190 / 0.534201 (-0.517011) | 0.367256 / 0.579283 (-0.212027) | 0.392709 / 0.434364 (-0.041655) | 0.426125 / 0.540337 (-0.114213) | 0.522612 / 1.386936 (-0.864324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009183 / 0.011353 (-0.002170) | 0.005232 / 0.011008 (-0.005776) | 0.120349 / 0.038508 (0.081841) | 0.044715 / 0.023109 (0.021606) | 0.361519 / 0.275898 (0.085621) | 0.463702 / 0.323480 (0.140223) | 0.005842 / 0.007986 (-0.002144) | 0.004041 / 0.004328 (-0.000288) | 0.096953 / 0.004250 (0.092703) | 0.070593 / 0.037052 (0.033540) | 0.409790 / 0.258489 (0.151301) | 0.477452 / 0.293841 (0.183611) | 0.045827 / 0.128546 (-0.082719) | 0.014038 / 0.075646 (-0.061608) | 0.421317 / 0.419271 (0.002045) | 0.065276 / 0.043533 (0.021743) | 0.360074 / 0.255139 (0.104935) | 0.409147 / 0.283200 (0.125947) | 0.032444 / 0.141683 (-0.109238) | 1.739257 / 1.452155 (0.287102) | 1.831408 / 1.492716 (0.338692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274852 / 0.018006 (0.256846) | 0.596320 / 0.000490 (0.595830) | 0.006399 / 0.000200 (0.006199) | 0.000133 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031400 / 0.037411 (-0.006012) | 0.127052 / 0.014526 (0.112526) | 0.134269 / 0.176557 (-0.042288) | 0.225998 / 0.737135 (-0.511137) | 0.150019 / 0.296338 (-0.146319) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654202 / 0.215209 (0.438993) | 6.216735 / 2.077655 (4.139081) | 2.440214 / 1.504120 (0.936094) | 2.150575 / 1.541195 (0.609380) | 2.124790 / 1.468490 (0.656300) | 0.923514 / 4.584777 (-3.661263) | 5.556924 / 3.745712 (1.811212) | 2.843886 / 5.269862 (-2.425975) | 1.834232 / 4.565676 (-2.731444) | 0.111735 / 0.424275 (-0.312540) | 0.014823 / 0.007607 (0.007216) | 0.820503 / 0.226044 (0.594459) | 7.887737 / 2.268929 (5.618809) | 3.120307 / 55.444624 (-52.324317) | 2.405856 / 6.876477 (-4.470621) | 2.411239 / 2.142072 (0.269167) | 1.071283 / 4.805227 (-3.733944) | 0.227738 / 6.500664 (-6.272926) | 0.073516 / 0.075469 (-0.001953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.531806 / 1.841788 (-0.309982) | 18.547661 / 8.074308 (10.473353) | 21.083922 / 10.191392 (10.892530) | 0.241706 / 0.680424 (-0.438718) | 0.034169 / 0.534201 (-0.500032) | 0.497514 / 0.579283 (-0.081769) | 0.599801 / 0.434364 (0.165437) | 0.576465 / 0.540337 (0.036127) | 0.673509 / 1.386936 (-0.713427) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007558 / 0.011353 (-0.003795) | 0.005001 / 0.011008 (-0.006008) | 0.093809 / 0.038508 (0.055301) | 0.039792 / 0.023109 (0.016683) | 0.456869 / 0.275898 (0.180971) | 0.493370 / 0.323480 (0.169891) | 0.005561 / 0.007986 (-0.002424) | 0.003982 / 0.004328 (-0.000346) | 0.085421 / 0.004250 (0.081170) | 0.059817 / 0.037052 (0.022765) | 0.468040 / 0.258489 (0.209550) | 0.514853 / 0.293841 (0.221012) | 0.044267 / 0.128546 (-0.084279) | 0.012674 / 0.075646 (-0.062972) | 0.098324 / 0.419271 (-0.320948) | 0.056604 / 0.043533 (0.013071) | 0.432200 / 0.255139 (0.177061) | 0.459812 / 0.283200 (0.176612) | 0.033872 / 0.141683 (-0.107811) | 1.618576 / 1.452155 (0.166421) | 1.676562 / 1.492716 (0.183846) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230625 / 0.018006 (0.212619) | 0.600558 / 0.000490 (0.600068) | 0.003419 / 0.000200 (0.003219) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026916 / 0.037411 (-0.010496) | 0.103003 / 0.014526 (0.088478) | 0.117078 / 0.176557 (-0.059478) | 0.169359 / 0.737135 (-0.567776) | 0.120305 / 0.296338 (-0.176034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616877 / 0.215209 (0.401668) | 6.157232 / 2.077655 (4.079577) | 2.869219 / 1.504120 (1.365099) | 2.381410 / 1.541195 (0.840216) | 2.417357 / 1.468490 (0.948867) | 0.914947 / 4.584777 (-3.669830) | 5.718526 / 3.745712 (1.972814) | 2.757253 / 5.269862 (-2.512609) | 1.794122 / 4.565676 (-2.771554) | 0.108423 / 0.424275 (-0.315852) | 0.013378 / 0.007607 (0.005771) | 0.831067 / 0.226044 (0.605023) | 8.478946 / 2.268929 (6.210018) | 3.685937 / 55.444624 (-51.758687) | 2.867472 / 6.876477 (-4.009005) | 2.895975 / 2.142072 (0.753903) | 1.137547 / 4.805227 (-3.667681) | 0.213891 / 6.500664 (-6.286773) | 0.075825 / 0.075469 (0.000356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621193 / 1.841788 (-0.220594) | 17.322110 / 8.074308 (9.247802) | 21.804016 / 10.191392 (11.612624) | 0.243692 / 0.680424 (-0.436732) | 0.030331 / 0.534201 (-0.503870) | 0.492186 / 0.579283 (-0.087097) | 0.632583 / 0.434364 (0.198219) | 0.576265 / 0.540337 (0.035927) | 0.713165 / 1.386936 (-0.673771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008916 / 0.011353 (-0.002437) | 0.004737 / 0.011008 (-0.006271) | 0.134271 / 0.038508 (0.095763) | 0.054472 / 0.023109 (0.031363) | 0.380942 / 0.275898 (0.105044) | 0.474138 / 0.323480 (0.150658) | 0.007917 / 0.007986 (-0.000068) | 0.003748 / 0.004328 (-0.000580) | 0.092765 / 0.004250 (0.088515) | 0.077873 / 0.037052 (0.040821) | 0.397533 / 0.258489 (0.139043) | 0.454737 / 0.293841 (0.160896) | 0.039901 / 0.128546 (-0.088645) | 0.010188 / 0.075646 (-0.065458) | 0.447312 / 0.419271 (0.028040) | 0.068684 / 0.043533 (0.025151) | 0.371554 / 0.255139 (0.116415) | 0.459655 / 0.283200 (0.176455) | 0.027157 / 0.141683 (-0.114526) | 1.874643 / 1.452155 (0.422488) | 2.014800 / 1.492716 (0.522083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227079 / 0.018006 (0.209073) | 0.483241 / 0.000490 (0.482751) | 0.012404 / 0.000200 (0.012204) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033135 / 0.037411 (-0.004277) | 0.137782 / 0.014526 (0.123257) | 0.142951 / 0.176557 (-0.033605) | 0.209825 / 0.737135 (-0.527311) | 0.152438 / 0.296338 (-0.143900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513066 / 0.215209 (0.297857) | 5.122776 / 2.077655 (3.045121) | 2.399270 / 1.504120 (0.895150) | 2.180143 / 1.541195 (0.638949) | 2.286395 / 1.468490 (0.817905) | 0.641866 / 4.584777 (-3.942911) | 4.694922 / 3.745712 (0.949210) | 2.543390 / 5.269862 (-2.726472) | 1.398592 / 4.565676 (-3.167084) | 0.088662 / 0.424275 (-0.335613) | 0.015854 / 0.007607 (0.008247) | 0.688891 / 0.226044 (0.462847) | 6.370148 / 2.268929 (4.101220) | 2.949974 / 55.444624 (-52.494650) | 2.538049 / 6.876477 (-4.338428) | 2.699380 / 2.142072 (0.557308) | 0.792670 / 4.805227 (-4.012557) | 0.169126 / 6.500664 (-6.331538) | 0.078511 / 0.075469 (0.003042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609119 / 1.841788 (-0.232669) | 18.785069 / 8.074308 (10.710761) | 16.670783 / 10.191392 (6.479391) | 0.213081 / 0.680424 (-0.467343) | 0.023904 / 0.534201 (-0.510296) | 0.567720 / 0.579283 (-0.011564) | 0.505806 / 0.434364 (0.071442) | 0.649466 / 0.540337 (0.109129) | 0.773174 / 1.386936 (-0.613762) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008036 / 0.011353 (-0.003317) | 0.004808 / 0.011008 (-0.006201) | 0.094316 / 0.038508 (0.055808) | 0.056174 / 0.023109 (0.033065) | 0.481618 / 0.275898 (0.205720) | 0.565300 / 0.323480 (0.241820) | 0.006339 / 0.007986 (-0.001646) | 0.003950 / 0.004328 (-0.000379) | 0.093389 / 0.004250 (0.089139) | 0.076163 / 0.037052 (0.039111) | 0.489013 / 0.258489 (0.230524) | 0.565451 / 0.293841 (0.271611) | 0.039392 / 0.128546 (-0.089155) | 0.010553 / 0.075646 (-0.065093) | 0.101406 / 0.419271 (-0.317865) | 0.062355 / 0.043533 (0.018822) | 0.470461 / 0.255139 (0.215322) | 0.502574 / 0.283200 (0.219375) | 0.030196 / 0.141683 (-0.111486) | 1.893926 / 1.452155 (0.441771) | 1.958902 / 1.492716 (0.466185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198074 / 0.018006 (0.180068) | 0.476828 / 0.000490 (0.476338) | 0.003457 / 0.000200 (0.003257) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037576 / 0.037411 (0.000165) | 0.146663 / 0.014526 (0.132138) | 0.152969 / 0.176557 (-0.023588) | 0.218683 / 0.737135 (-0.518452) | 0.161552 / 0.296338 (-0.134786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.525988 / 0.215209 (0.310779) | 5.234673 / 2.077655 (3.157018) | 2.571668 / 1.504120 (1.067548) | 2.339760 / 1.541195 (0.798565) | 2.422886 / 1.468490 (0.954395) | 0.651537 / 4.584777 (-3.933240) | 4.811148 / 3.745712 (1.065436) | 4.451165 / 5.269862 (-0.818697) | 2.016283 / 4.565676 (-2.549394) | 0.096393 / 0.424275 (-0.327882) | 0.015222 / 0.007607 (0.007615) | 0.739132 / 0.226044 (0.513087) | 6.813327 / 2.268929 (4.544399) | 3.169018 / 55.444624 (-52.275606) | 2.783120 / 6.876477 (-4.093356) | 2.918979 / 2.142072 (0.776907) | 0.797476 / 4.805227 (-4.007751) | 0.171038 / 6.500664 (-6.329626) | 0.079878 / 0.075469 (0.004409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595082 / 1.841788 (-0.246705) | 19.685844 / 8.074308 (11.611536) | 17.518989 / 10.191392 (7.327597) | 0.220015 / 0.680424 (-0.460409) | 0.026351 / 0.534201 (-0.507850) | 0.578977 / 0.579283 (-0.000306) | 0.549564 / 0.434364 (0.115200) | 0.667564 / 0.540337 (0.127227) | 0.802121 / 1.386936 (-0.584815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]

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[ "Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5" ]

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[ "Thanks for reporting, @wangyuxinwhy. " ]

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[ "I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.", "All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006134 / 0.011353 (-0.005219) | 0.003816 / 0.011008 (-0.007193) | 0.098226 / 0.038508 (0.059718) | 0.036830 / 0.023109 (0.013721) | 0.314551 / 0.275898 (0.038653) | 0.372251 / 0.323480 (0.048771) | 0.004762 / 0.007986 (-0.003224) | 0.003041 / 0.004328 (-0.001287) | 0.077651 / 0.004250 (0.073401) | 0.052445 / 0.037052 (0.015393) | 0.324632 / 0.258489 (0.066143) | 0.365724 / 0.293841 (0.071883) | 0.028069 / 0.128546 (-0.100477) | 0.008444 / 0.075646 (-0.067203) | 0.312767 / 0.419271 (-0.106505) | 0.047773 / 0.043533 (0.004240) | 0.305317 / 0.255139 (0.050178) | 0.332007 / 0.283200 (0.048807) | 0.018985 / 0.141683 (-0.122698) | 1.538022 / 1.452155 (0.085868) | 1.575898 / 1.492716 (0.083182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204780 / 0.018006 (0.186774) | 0.428125 / 0.000490 (0.427635) | 0.003454 / 0.000200 (0.003254) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025064 / 0.037411 (-0.012348) | 0.099419 / 0.014526 (0.084893) | 0.111068 / 0.176557 (-0.065489) | 0.169775 / 0.737135 (-0.567361) | 0.112067 / 0.296338 (-0.184271) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429642 / 0.215209 (0.214433) | 4.275556 / 2.077655 (2.197901) | 1.914658 / 1.504120 (0.410539) | 1.706556 / 1.541195 (0.165361) | 1.754228 / 1.468490 (0.285738) | 0.563669 / 4.584777 (-4.021108) | 3.391501 / 3.745712 (-0.354211) | 1.791517 / 5.269862 (-3.478345) | 1.030704 / 4.565676 (-3.534973) | 0.070882 / 0.424275 (-0.353393) | 0.011351 / 0.007607 (0.003744) | 0.529438 / 0.226044 (0.303394) | 5.294316 / 2.268929 (3.025387) | 2.344653 / 55.444624 (-53.099972) | 1.997468 / 6.876477 (-4.879009) | 2.108932 / 2.142072 (-0.033140) | 0.676794 / 4.805227 (-4.128433) | 0.135058 / 6.500664 (-6.365607) | 0.065857 / 0.075469 (-0.009612) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231864 / 1.841788 (-0.609924) | 13.986694 / 8.074308 (5.912386) | 13.306600 / 10.191392 (3.115208) | 0.145520 / 0.680424 (-0.534904) | 0.016717 / 0.534201 (-0.517484) | 0.366303 / 0.579283 (-0.212980) | 0.391637 / 0.434364 (-0.042727) | 0.425445 / 0.540337 (-0.114892) | 0.507719 / 1.386936 (-0.879217) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006236 / 0.011353 (-0.005116) | 0.003766 / 0.011008 (-0.007242) | 0.076794 / 0.038508 (0.038286) | 0.037210 / 0.023109 (0.014101) | 0.378387 / 0.275898 (0.102489) | 0.425456 / 0.323480 (0.101977) | 0.004694 / 0.007986 (-0.003291) | 0.002921 / 0.004328 (-0.001407) | 0.076985 / 0.004250 (0.072735) | 0.052188 / 0.037052 (0.015136) | 0.394385 / 0.258489 (0.135896) | 0.432527 / 0.293841 (0.138686) | 0.029091 / 0.128546 (-0.099455) | 0.008364 / 0.075646 (-0.067282) | 0.082583 / 0.419271 (-0.336689) | 0.042928 / 0.043533 (-0.000605) | 0.375321 / 0.255139 (0.120182) | 0.391719 / 0.283200 (0.108519) | 0.019388 / 0.141683 (-0.122295) | 1.550644 / 1.452155 (0.098489) | 1.604882 / 1.492716 (0.112166) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236859 / 0.018006 (0.218853) | 0.418528 / 0.000490 (0.418039) | 0.000388 / 0.000200 (0.000188) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025548 / 0.037411 (-0.011863) | 0.100644 / 0.014526 (0.086118) | 0.109102 / 0.176557 (-0.067455) | 0.161694 / 0.737135 (-0.575441) | 0.112088 / 0.296338 (-0.184250) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.484128 / 0.215209 (0.268919) | 4.849952 / 2.077655 (2.772297) | 2.512769 / 1.504120 (1.008649) | 2.303295 / 1.541195 (0.762100) | 2.356699 / 1.468490 (0.888209) | 0.564181 / 4.584777 (-4.020596) | 3.421393 / 3.745712 (-0.324319) | 2.570875 / 5.269862 (-2.698987) | 1.474307 / 4.565676 (-3.091370) | 0.068035 / 0.424275 (-0.356240) | 0.011300 / 0.007607 (0.003693) | 0.587867 / 0.226044 (0.361823) | 5.862447 / 2.268929 (3.593519) | 3.004017 / 55.444624 (-52.440607) | 2.664989 / 6.876477 (-4.211488) | 2.740020 / 2.142072 (0.597948) | 0.680840 / 4.805227 (-4.124387) | 0.137001 / 6.500664 (-6.363663) | 0.068098 / 0.075469 (-0.007371) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.297362 / 1.841788 (-0.544426) | 14.207891 / 8.074308 (6.133583) | 14.087562 / 10.191392 (3.896170) | 0.149514 / 0.680424 (-0.530910) | 0.016566 / 0.534201 (-0.517635) | 0.367602 / 0.579283 (-0.211681) | 0.400692 / 0.434364 (-0.033671) | 0.432907 / 0.540337 (-0.107431) | 0.525924 / 1.386936 (-0.861012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006223 / 0.011353 (-0.005130) | 0.003672 / 0.011008 (-0.007336) | 0.097451 / 0.038508 (0.058943) | 0.036243 / 0.023109 (0.013133) | 0.375650 / 0.275898 (0.099752) | 0.431652 / 0.323480 (0.108172) | 0.004758 / 0.007986 (-0.003227) | 0.002941 / 0.004328 (-0.001387) | 0.077383 / 0.004250 (0.073132) | 0.055342 / 0.037052 (0.018289) | 0.390335 / 0.258489 (0.131846) | 0.427867 / 0.293841 (0.134026) | 0.027619 / 0.128546 (-0.100927) | 0.008244 / 0.075646 (-0.067402) | 0.313499 / 0.419271 (-0.105773) | 0.054987 / 0.043533 (0.011454) | 0.394044 / 0.255139 (0.138905) | 0.398784 / 0.283200 (0.115584) | 0.026499 / 0.141683 (-0.115184) | 1.496907 / 1.452155 (0.044753) | 1.554465 / 1.492716 (0.061749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241197 / 0.018006 (0.223190) | 0.427856 / 0.000490 (0.427366) | 0.006264 / 0.000200 (0.006065) | 0.000218 / 0.000054 (0.000164) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025550 / 0.037411 (-0.011862) | 0.104426 / 0.014526 (0.089901) | 0.110310 / 0.176557 (-0.066246) | 0.173813 / 0.737135 (-0.563322) | 0.112129 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458806 / 0.215209 (0.243597) | 4.576351 / 2.077655 (2.498697) | 2.265670 / 1.504120 (0.761550) | 2.073230 / 1.541195 (0.532035) | 2.135283 / 1.468490 (0.666793) | 0.562506 / 4.584777 (-4.022271) | 3.375101 / 3.745712 (-0.370611) | 1.734393 / 5.269862 (-3.535469) | 1.026622 / 4.565676 (-3.539054) | 0.068144 / 0.424275 (-0.356131) | 0.011092 / 0.007607 (0.003485) | 0.562779 / 0.226044 (0.336734) | 5.608256 / 2.268929 (3.339328) | 2.706468 / 55.444624 (-52.738157) | 2.381607 / 6.876477 (-4.494869) | 2.451027 / 2.142072 (0.308954) | 0.671590 / 4.805227 (-4.133637) | 0.135749 / 6.500664 (-6.364915) | 0.065389 / 0.075469 (-0.010080) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244806 / 1.841788 (-0.596981) | 14.042150 / 8.074308 (5.967841) | 14.246612 / 10.191392 (4.055220) | 0.134309 / 0.680424 (-0.546114) | 0.017082 / 0.534201 (-0.517119) | 0.366043 / 0.579283 (-0.213240) | 0.400748 / 0.434364 (-0.033616) | 0.425695 / 0.540337 (-0.114643) | 0.509355 / 1.386936 (-0.877581) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006134 / 0.011353 (-0.005219) | 0.003980 / 0.011008 (-0.007028) | 0.078353 / 0.038508 (0.039845) | 0.038011 / 0.023109 (0.014902) | 0.375784 / 0.275898 (0.099886) | 0.433619 / 0.323480 (0.110139) | 0.004897 / 0.007986 (-0.003088) | 0.002981 / 0.004328 (-0.001347) | 0.077362 / 0.004250 (0.073112) | 0.056108 / 0.037052 (0.019056) | 0.395984 / 0.258489 (0.137495) | 0.427397 / 0.293841 (0.133556) | 0.029325 / 0.128546 (-0.099221) | 0.008498 / 0.075646 (-0.067148) | 0.082478 / 0.419271 (-0.336794) | 0.044085 / 0.043533 (0.000552) | 0.389923 / 0.255139 (0.134784) | 0.391180 / 0.283200 (0.107980) | 0.022452 / 0.141683 (-0.119231) | 1.507758 / 1.452155 (0.055603) | 1.530459 / 1.492716 (0.037743) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230928 / 0.018006 (0.212922) | 0.408484 / 0.000490 (0.407995) | 0.000806 / 0.000200 (0.000606) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025183 / 0.037411 (-0.012228) | 0.102292 / 0.014526 (0.087766) | 0.108142 / 0.176557 (-0.068415) | 0.161172 / 0.737135 (-0.575963) | 0.114476 / 0.296338 (-0.181862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482978 / 0.215209 (0.267769) | 4.816103 / 2.077655 (2.738448) | 2.505567 / 1.504120 (1.001447) | 2.302598 / 1.541195 (0.761404) | 2.371238 / 1.468490 (0.902748) | 0.567467 / 4.584777 (-4.017310) | 3.363407 / 3.745712 (-0.382306) | 1.746213 / 5.269862 (-3.523649) | 1.035468 / 4.565676 (-3.530208) | 0.068431 / 0.424275 (-0.355844) | 0.011069 / 0.007607 (0.003462) | 0.598241 / 0.226044 (0.372196) | 5.953927 / 2.268929 (3.684999) | 3.007493 / 55.444624 (-52.437132) | 2.629399 / 6.876477 (-4.247078) | 2.737201 / 2.142072 (0.595129) | 0.682456 / 4.805227 (-4.122771) | 0.137613 / 6.500664 (-6.363051) | 0.067941 / 0.075469 (-0.007528) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306015 / 1.841788 (-0.535772) | 14.359240 / 8.074308 (6.284932) | 14.187601 / 10.191392 (3.996209) | 0.138612 / 0.680424 (-0.541812) | 0.016708 / 0.534201 (-0.517493) | 0.366365 / 0.579283 (-0.212918) | 0.396982 / 0.434364 (-0.037382) | 0.426939 / 0.540337 (-0.113398) | 0.520064 / 1.386936 (-0.866872) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n", "They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.", "> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg", "> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007644 / 0.011353 (-0.003709) | 0.004667 / 0.011008 (-0.006341) | 0.117347 / 0.038508 (0.078839) | 0.050620 / 0.023109 (0.027510) | 0.415402 / 0.275898 (0.139504) | 0.485898 / 0.323480 (0.162418) | 0.005848 / 0.007986 (-0.002138) | 0.003736 / 0.004328 (-0.000592) | 0.089798 / 0.004250 (0.085547) | 0.069344 / 0.037052 (0.032292) | 0.441684 / 0.258489 (0.183195) | 0.468972 / 0.293841 (0.175131) | 0.036637 / 0.128546 (-0.091909) | 0.010219 / 0.075646 (-0.065427) | 0.394293 / 0.419271 (-0.024978) | 0.061462 / 0.043533 (0.017929) | 0.409448 / 0.255139 (0.154309) | 0.431557 / 0.283200 (0.148358) | 0.027795 / 0.141683 (-0.113888) | 1.837844 / 1.452155 (0.385690) | 1.862683 / 1.492716 (0.369967) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230500 / 0.018006 (0.212494) | 0.483139 / 0.000490 (0.482649) | 0.006517 / 0.000200 (0.006317) | 0.000143 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033152 / 0.037411 (-0.004259) | 0.133673 / 0.014526 (0.119147) | 0.143853 / 0.176557 (-0.032704) | 0.215254 / 0.737135 (-0.521882) | 0.150676 / 0.296338 (-0.145662) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503796 / 0.215209 (0.288587) | 5.049981 / 2.077655 (2.972326) | 2.399427 / 1.504120 (0.895307) | 2.167635 / 1.541195 (0.626441) | 2.257448 / 1.468490 (0.788958) | 0.641298 / 4.584777 (-3.943479) | 4.828676 / 3.745712 (1.082964) | 4.346069 / 5.269862 (-0.923793) | 2.103890 / 4.565676 (-2.461786) | 0.079115 / 0.424275 (-0.345160) | 0.013377 / 0.007607 (0.005770) | 0.621207 / 0.226044 (0.395162) | 6.190939 / 2.268929 (3.922011) | 2.920129 / 55.444624 (-52.524495) | 2.549225 / 6.876477 (-4.327252) | 2.719221 / 2.142072 (0.577149) | 0.790949 / 4.805227 (-4.014278) | 0.172032 / 6.500664 (-6.328632) | 0.077779 / 0.075469 (0.002310) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432572 / 1.841788 (-0.409216) | 21.000031 / 8.074308 (12.925723) | 17.555093 / 10.191392 (7.363701) | 0.166646 / 0.680424 (-0.513778) | 0.020451 / 0.534201 (-0.513750) | 0.488767 / 0.579283 (-0.090516) | 0.737036 / 0.434364 (0.302672) | 0.621694 / 0.540337 (0.081356) | 0.732074 / 1.386936 (-0.654862) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008198 / 0.011353 (-0.003155) | 0.004987 / 0.011008 (-0.006021) | 0.090714 / 0.038508 (0.052206) | 0.053379 / 0.023109 (0.030270) | 0.425199 / 0.275898 (0.149301) | 0.514036 / 0.323480 (0.190556) | 0.006043 / 0.007986 (-0.001943) | 0.003888 / 0.004328 (-0.000441) | 0.088294 / 0.004250 (0.084043) | 0.073024 / 0.037052 (0.035971) | 0.435983 / 0.258489 (0.177494) | 0.514293 / 0.293841 (0.220452) | 0.039451 / 0.128546 (-0.089095) | 0.010439 / 0.075646 (-0.065207) | 0.096885 / 0.419271 (-0.322387) | 0.060165 / 0.043533 (0.016632) | 0.421053 / 0.255139 (0.165914) | 0.455545 / 0.283200 (0.172345) | 0.027234 / 0.141683 (-0.114449) | 1.768975 / 1.452155 (0.316820) | 1.842853 / 1.492716 (0.350137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278940 / 0.018006 (0.260933) | 0.480709 / 0.000490 (0.480219) | 0.000436 / 0.000200 (0.000236) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034900 / 0.037411 (-0.002511) | 0.144893 / 0.014526 (0.130368) | 0.149567 / 0.176557 (-0.026989) | 0.213200 / 0.737135 (-0.523935) | 0.156735 / 0.296338 (-0.139604) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.535897 / 0.215209 (0.320687) | 5.336998 / 2.077655 (3.259343) | 2.685854 / 1.504120 (1.181734) | 2.470177 / 1.541195 (0.928983) | 2.547495 / 1.468490 (1.079004) | 0.642830 / 4.584777 (-3.941947) | 4.595866 / 3.745712 (0.850154) | 2.186696 / 5.269862 (-3.083165) | 1.317969 / 4.565676 (-3.247708) | 0.079268 / 0.424275 (-0.345007) | 0.013792 / 0.007607 (0.006185) | 0.662236 / 0.226044 (0.436192) | 6.604775 / 2.268929 (4.335847) | 3.355888 / 55.444624 (-52.088736) | 2.968911 / 6.876477 (-3.907565) | 3.121862 / 2.142072 (0.979790) | 0.794752 / 4.805227 (-4.010475) | 0.170800 / 6.500664 (-6.329864) | 0.078393 / 0.075469 (0.002924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.601605 / 1.841788 (-0.240183) | 20.743553 / 8.074308 (12.669245) | 17.543968 / 10.191392 (7.352576) | 0.221884 / 0.680424 (-0.458540) | 0.020779 / 0.534201 (-0.513422) | 0.479677 / 0.579283 (-0.099606) | 0.516207 / 0.434364 (0.081843) | 0.564046 / 0.540337 (0.023709) | 0.711336 / 1.386936 (-0.675600) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n", "Yes, sounds great! Thanks", "yup" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009725 / 0.011353 (-0.001628) | 0.006014 / 0.011008 (-0.004994) | 0.136039 / 0.038508 (0.097531) | 0.049685 / 0.023109 (0.026576) | 0.492967 / 0.275898 (0.217068) | 0.553775 / 0.323480 (0.230295) | 0.007421 / 0.007986 (-0.000564) | 0.004686 / 0.004328 (0.000357) | 0.106639 / 0.004250 (0.102389) | 0.073483 / 0.037052 (0.036431) | 0.507194 / 0.258489 (0.248705) | 0.535760 / 0.293841 (0.241919) | 0.049666 / 0.128546 (-0.078880) | 0.014139 / 0.075646 (-0.061507) | 0.435459 / 0.419271 (0.016188) | 0.076026 / 0.043533 (0.032493) | 0.454542 / 0.255139 (0.199403) | 0.512724 / 0.283200 (0.229524) | 0.034969 / 0.141683 (-0.106713) | 1.881048 / 1.452155 (0.428893) | 1.959915 / 1.492716 (0.467199) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265322 / 0.018006 (0.247316) | 0.573963 / 0.000490 (0.573474) | 0.017493 / 0.000200 (0.017293) | 0.000637 / 0.000054 (0.000582) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028712 / 0.037411 (-0.008699) | 0.149554 / 0.014526 (0.135029) | 0.130013 / 0.176557 (-0.046544) | 0.203408 / 0.737135 (-0.533727) | 0.144778 / 0.296338 (-0.151561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664198 / 0.215209 (0.448989) | 6.418054 / 2.077655 (4.340399) | 2.602338 / 1.504120 (1.098219) | 2.212992 / 1.541195 (0.671797) | 2.214309 / 1.468490 (0.745819) | 0.914772 / 4.584777 (-3.670005) | 5.824831 / 3.745712 (2.079119) | 2.865381 / 5.269862 (-2.404481) | 1.906020 / 4.565676 (-2.659657) | 0.106947 / 0.424275 (-0.317328) | 0.013467 / 0.007607 (0.005860) | 0.834556 / 0.226044 (0.608512) | 8.237078 / 2.268929 (5.968150) | 3.380919 / 55.444624 (-52.063705) | 2.656713 / 6.876477 (-4.219764) | 2.834941 / 2.142072 (0.692869) | 1.151241 / 4.805227 (-3.653986) | 0.220860 / 6.500664 (-6.279804) | 0.080781 / 0.075469 (0.005312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655128 / 1.841788 (-0.186660) | 18.696108 / 8.074308 (10.621800) | 22.882108 / 10.191392 (12.690716) | 0.236041 / 0.680424 (-0.444383) | 0.031073 / 0.534201 (-0.503128) | 0.525263 / 0.579283 (-0.054021) | 0.632933 / 0.434364 (0.198569) | 0.707228 / 0.540337 (0.166890) | 0.753508 / 1.386936 (-0.633428) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009875 / 0.011353 (-0.001478) | 0.005135 / 0.011008 (-0.005873) | 0.101307 / 0.038508 (0.062799) | 0.044895 / 0.023109 (0.021786) | 0.497824 / 0.275898 (0.221926) | 0.573098 / 0.323480 (0.249618) | 0.006669 / 0.007986 (-0.001317) | 0.004289 / 0.004328 (-0.000039) | 0.105824 / 0.004250 (0.101573) | 0.061002 / 0.037052 (0.023950) | 0.510127 / 0.258489 (0.251638) | 0.581387 / 0.293841 (0.287546) | 0.052843 / 0.128546 (-0.075703) | 0.015506 / 0.075646 (-0.060140) | 0.116057 / 0.419271 (-0.303215) | 0.063444 / 0.043533 (0.019912) | 0.479366 / 0.255139 (0.224227) | 0.518419 / 0.283200 (0.235220) | 0.034876 / 0.141683 (-0.106806) | 2.018446 / 1.452155 (0.566292) | 1.960755 / 1.492716 (0.468039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269077 / 0.018006 (0.251070) | 0.606059 / 0.000490 (0.605569) | 0.000488 / 0.000200 (0.000288) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032465 / 0.037411 (-0.004946) | 0.136517 / 0.014526 (0.121991) | 0.147740 / 0.176557 (-0.028816) | 0.193802 / 0.737135 (-0.543334) | 0.151876 / 0.296338 (-0.144462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.709866 / 0.215209 (0.494657) | 6.848193 / 2.077655 (4.770538) | 3.310853 / 1.504120 (1.806733) | 2.940813 / 1.541195 (1.399619) | 2.934934 / 1.468490 (1.466444) | 0.927104 / 4.584777 (-3.657673) | 5.921607 / 3.745712 (2.175895) | 4.926558 / 5.269862 (-0.343303) | 2.853269 / 4.565676 (-1.712407) | 0.120278 / 0.424275 (-0.303998) | 0.015468 / 0.007607 (0.007861) | 0.820509 / 0.226044 (0.594464) | 8.263136 / 2.268929 (5.994208) | 3.780214 / 55.444624 (-51.664410) | 3.108482 / 6.876477 (-3.767995) | 3.101544 / 2.142072 (0.959471) | 1.165539 / 4.805227 (-3.639688) | 0.229215 / 6.500664 (-6.271449) | 0.079862 / 0.075469 (0.004393) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.775071 / 1.841788 (-0.066717) | 19.327621 / 8.074308 (11.253313) | 23.057537 / 10.191392 (12.866145) | 0.250649 / 0.680424 (-0.429775) | 0.029767 / 0.534201 (-0.504434) | 0.554774 / 0.579283 (-0.024509) | 0.651919 / 0.434364 (0.217555) | 0.651641 / 0.540337 (0.111304) | 0.762386 / 1.386936 (-0.624550) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fdc3ce7060366f480621e8640903c9ab476164e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005997 / 0.011353 (-0.005356) | 0.003892 / 0.011008 (-0.007116) | 0.098020 / 0.038508 (0.059512) | 0.042584 / 0.023109 (0.019475) | 0.317909 / 0.275898 (0.042011) | 0.395042 / 0.323480 (0.071563) | 0.005358 / 0.007986 (-0.002628) | 0.003266 / 0.004328 (-0.001062) | 0.076698 / 0.004250 (0.072447) | 0.062331 / 0.037052 (0.025279) | 0.334900 / 0.258489 (0.076411) | 0.379355 / 0.293841 (0.085514) | 0.030815 / 0.128546 (-0.097731) | 0.008596 / 0.075646 (-0.067050) | 0.327739 / 0.419271 (-0.091533) | 0.054061 / 0.043533 (0.010528) | 0.311044 / 0.255139 (0.055905) | 0.336705 / 0.283200 (0.053506) | 0.022785 / 0.141683 (-0.118898) | 1.516793 / 1.452155 (0.064639) | 1.590435 / 1.492716 (0.097719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289157 / 0.018006 (0.271151) | 0.531074 / 0.000490 (0.530585) | 0.004672 / 0.000200 (0.004472) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026173 / 0.037411 (-0.011238) | 0.105723 / 0.014526 (0.091197) | 0.118010 / 0.176557 (-0.058547) | 0.178062 / 0.737135 (-0.559073) | 0.120059 / 0.296338 (-0.176279) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410870 / 0.215209 (0.195661) | 4.042183 / 2.077655 (1.964528) | 1.830059 / 1.504120 (0.325939) | 1.638996 / 1.541195 (0.097802) | 1.701368 / 1.468490 (0.232878) | 0.529915 / 4.584777 (-4.054861) | 3.693308 / 3.745712 (-0.052404) | 1.827875 / 5.269862 (-3.441986) | 1.063237 / 4.565676 (-3.502440) | 0.065368 / 0.424275 (-0.358907) | 0.010986 / 0.007607 (0.003379) | 0.509399 / 0.226044 (0.283354) | 5.092739 / 2.268929 (2.823810) | 2.293490 / 55.444624 (-53.151135) | 1.958742 / 6.876477 (-4.917735) | 2.024985 / 2.142072 (-0.117088) | 0.646978 / 4.805227 (-4.158249) | 0.138616 / 6.500664 (-6.362048) | 0.062101 / 0.075469 (-0.013368) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202016 / 1.841788 (-0.639772) | 14.493204 / 8.074308 (6.418896) | 12.992160 / 10.191392 (2.800768) | 0.188922 / 0.680424 (-0.491502) | 0.017594 / 0.534201 (-0.516606) | 0.399917 / 0.579283 (-0.179367) | 0.429760 / 0.434364 (-0.004604) | 0.497906 / 0.540337 (-0.042431) | 0.608745 / 1.386936 (-0.778191) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006164 / 0.011353 (-0.005189) | 0.003980 / 0.011008 (-0.007028) | 0.074676 / 0.038508 (0.036168) | 0.041337 / 0.023109 (0.018228) | 0.400981 / 0.275898 (0.125083) | 0.448791 / 0.323480 (0.125312) | 0.004063 / 0.007986 (-0.003923) | 0.004443 / 0.004328 (0.000114) | 0.075011 / 0.004250 (0.070760) | 0.056494 / 0.037052 (0.019441) | 0.402054 / 0.258489 (0.143565) | 0.446122 / 0.293841 (0.152281) | 0.031752 / 0.128546 (-0.096794) | 0.008835 / 0.075646 (-0.066811) | 0.081226 / 0.419271 (-0.338046) | 0.051501 / 0.043533 (0.007969) | 0.383674 / 0.255139 (0.128535) | 0.405524 / 0.283200 (0.122325) | 0.025929 / 0.141683 (-0.115754) | 1.492985 / 1.452155 (0.040830) | 1.541601 / 1.492716 (0.048885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305149 / 0.018006 (0.287142) | 0.497259 / 0.000490 (0.496770) | 0.000420 / 0.000200 (0.000220) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027933 / 0.037411 (-0.009479) | 0.111900 / 0.014526 (0.097374) | 0.124879 / 0.176557 (-0.051678) | 0.178952 / 0.737135 (-0.558184) | 0.127698 / 0.296338 (-0.168640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448525 / 0.215209 (0.233316) | 4.486791 / 2.077655 (2.409137) | 2.256687 / 1.504120 (0.752567) | 2.061078 / 1.541195 (0.519884) | 2.078924 / 1.468490 (0.610434) | 0.534412 / 4.584777 (-4.050365) | 3.721098 / 3.745712 (-0.024614) | 1.818735 / 5.269862 (-3.451127) | 1.104198 / 4.565676 (-3.461479) | 0.066277 / 0.424275 (-0.357998) | 0.011441 / 0.007607 (0.003834) | 0.550140 / 0.226044 (0.324095) | 5.498079 / 2.268929 (3.229150) | 2.717398 / 55.444624 (-52.727227) | 2.410194 / 6.876477 (-4.466283) | 2.405304 / 2.142072 (0.263231) | 0.665432 / 4.805227 (-4.139796) | 0.141488 / 6.500664 (-6.359177) | 0.064051 / 0.075469 (-0.011419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272334 / 1.841788 (-0.569454) | 14.901608 / 8.074308 (6.827300) | 14.287857 / 10.191392 (4.096465) | 0.165337 / 0.680424 (-0.515086) | 0.017402 / 0.534201 (-0.516799) | 0.398120 / 0.579283 (-0.181163) | 0.416539 / 0.434364 (-0.017825) | 0.463890 / 0.540337 (-0.076447) | 0.567909 / 1.386936 (-0.819027) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#504ec0f2e00ee38e0993ed1e4f1e10f1eefaea0d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009434 / 0.011353 (-0.001919) | 0.005567 / 0.011008 (-0.005441) | 0.122652 / 0.038508 (0.084144) | 0.050177 / 0.023109 (0.027067) | 0.384292 / 0.275898 (0.108394) | 0.446608 / 0.323480 (0.123128) | 0.006502 / 0.007986 (-0.001484) | 0.004523 / 0.004328 (0.000194) | 0.100581 / 0.004250 (0.096331) | 0.073615 / 0.037052 (0.036563) | 0.420179 / 0.258489 (0.161690) | 0.474631 / 0.293841 (0.180790) | 0.047942 / 0.128546 (-0.080604) | 0.013864 / 0.075646 (-0.061783) | 0.419384 / 0.419271 (0.000112) | 0.088317 / 0.043533 (0.044784) | 0.379620 / 0.255139 (0.124481) | 0.412639 / 0.283200 (0.129440) | 0.048947 / 0.141683 (-0.092736) | 1.823498 / 1.452155 (0.371343) | 1.966629 / 1.492716 (0.473913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300669 / 0.018006 (0.282663) | 0.593499 / 0.000490 (0.593009) | 0.007247 / 0.000200 (0.007047) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030556 / 0.037411 (-0.006856) | 0.119252 / 0.014526 (0.104726) | 0.131403 / 0.176557 (-0.045153) | 0.201845 / 0.737135 (-0.535291) | 0.139350 / 0.296338 (-0.156989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.652400 / 0.215209 (0.437191) | 6.536540 / 2.077655 (4.458886) | 2.644565 / 1.504120 (1.140445) | 2.245181 / 1.541195 (0.703986) | 2.316030 / 1.468490 (0.847540) | 0.922535 / 4.584777 (-3.662242) | 5.469065 / 3.745712 (1.723353) | 2.800489 / 5.269862 (-2.469373) | 1.749042 / 4.565676 (-2.816635) | 0.108444 / 0.424275 (-0.315831) | 0.015651 / 0.007607 (0.008044) | 0.846085 / 0.226044 (0.620041) | 8.018460 / 2.268929 (5.749531) | 3.338710 / 55.444624 (-52.105914) | 2.675998 / 6.876477 (-4.200479) | 2.918550 / 2.142072 (0.776478) | 1.135145 / 4.805227 (-3.670082) | 0.215165 / 6.500664 (-6.285499) | 0.082066 / 0.075469 (0.006597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561661 / 1.841788 (-0.280127) | 18.519035 / 8.074308 (10.444727) | 19.046300 / 10.191392 (8.854908) | 0.236890 / 0.680424 (-0.443534) | 0.027681 / 0.534201 (-0.506520) | 0.511998 / 0.579283 (-0.067285) | 0.591627 / 0.434364 (0.157264) | 0.562021 / 0.540337 (0.021683) | 0.679354 / 1.386936 (-0.707582) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009643 / 0.011353 (-0.001710) | 0.005768 / 0.011008 (-0.005241) | 0.104430 / 0.038508 (0.065922) | 0.050044 / 0.023109 (0.026935) | 0.464117 / 0.275898 (0.188219) | 0.518439 / 0.323480 (0.194959) | 0.006935 / 0.007986 (-0.001051) | 0.004316 / 0.004328 (-0.000013) | 0.094330 / 0.004250 (0.090080) | 0.071451 / 0.037052 (0.034399) | 0.492248 / 0.258489 (0.233759) | 0.555740 / 0.293841 (0.261899) | 0.047836 / 0.128546 (-0.080711) | 0.014788 / 0.075646 (-0.060859) | 0.107590 / 0.419271 (-0.311682) | 0.064396 / 0.043533 (0.020863) | 0.451529 / 0.255139 (0.196390) | 0.475025 / 0.283200 (0.191826) | 0.040006 / 0.141683 (-0.101677) | 1.797107 / 1.452155 (0.344953) | 1.879261 / 1.492716 (0.386545) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298458 / 0.018006 (0.280451) | 0.613022 / 0.000490 (0.612532) | 0.003582 / 0.000200 (0.003382) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.123286 / 0.014526 (0.108760) | 0.132070 / 0.176557 (-0.044486) | 0.190883 / 0.737135 (-0.546252) | 0.138526 / 0.296338 (-0.157812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.666908 / 0.215209 (0.451699) | 6.489035 / 2.077655 (4.411381) | 2.897027 / 1.504120 (1.392907) | 2.565150 / 1.541195 (1.023956) | 2.504827 / 1.468490 (1.036336) | 0.916112 / 4.584777 (-3.668665) | 5.651751 / 3.745712 (1.906039) | 2.743382 / 5.269862 (-2.526479) | 1.773338 / 4.565676 (-2.792338) | 0.128764 / 0.424275 (-0.295511) | 0.013140 / 0.007607 (0.005533) | 0.803281 / 0.226044 (0.577236) | 8.258874 / 2.268929 (5.989945) | 3.633260 / 55.444624 (-51.811364) | 2.878827 / 6.876477 (-3.997649) | 2.977178 / 2.142072 (0.835106) | 1.130467 / 4.805227 (-3.674760) | 0.226381 / 6.500664 (-6.274283) | 0.081550 / 0.075469 (0.006081) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.842927 / 1.841788 (0.001139) | 18.411520 / 8.074308 (10.337212) | 21.118228 / 10.191392 (10.926836) | 0.231526 / 0.680424 (-0.448898) | 0.029300 / 0.534201 (-0.504901) | 0.527450 / 0.579283 (-0.051834) | 0.618873 / 0.434364 (0.184509) | 0.593314 / 0.540337 (0.052976) | 0.734430 / 1.386936 (-0.652506) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d2b8854c265b4dc202e480427890f472b34ea15 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005969 / 0.011353 (-0.005384) | 0.003687 / 0.011008 (-0.007321) | 0.100843 / 0.038508 (0.062335) | 0.036912 / 0.023109 (0.013803) | 0.312389 / 0.275898 (0.036491) | 0.370335 / 0.323480 (0.046855) | 0.003434 / 0.007986 (-0.004552) | 0.003710 / 0.004328 (-0.000619) | 0.076899 / 0.004250 (0.072648) | 0.053647 / 0.037052 (0.016594) | 0.324825 / 0.258489 (0.066336) | 0.367711 / 0.293841 (0.073870) | 0.028079 / 0.128546 (-0.100467) | 0.008326 / 0.075646 (-0.067320) | 0.312342 / 0.419271 (-0.106930) | 0.047423 / 0.043533 (0.003890) | 0.321063 / 0.255139 (0.065924) | 0.336508 / 0.283200 (0.053308) | 0.019973 / 0.141683 (-0.121710) | 1.529334 / 1.452155 (0.077179) | 1.573746 / 1.492716 (0.081030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210849 / 0.018006 (0.192843) | 0.418798 / 0.000490 (0.418309) | 0.007347 / 0.000200 (0.007147) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022718 / 0.037411 (-0.014694) | 0.098400 / 0.014526 (0.083874) | 0.106590 / 0.176557 (-0.069967) | 0.168460 / 0.737135 (-0.568675) | 0.108401 / 0.296338 (-0.187938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443066 / 0.215209 (0.227857) | 4.416658 / 2.077655 (2.339003) | 2.088844 / 1.504120 (0.584724) | 1.879564 / 1.541195 (0.338369) | 1.933815 / 1.468490 (0.465325) | 0.565085 / 4.584777 (-4.019692) | 3.412440 / 3.745712 (-0.333273) | 1.754686 / 5.269862 (-3.515175) | 1.024576 / 4.565676 (-3.541100) | 0.067909 / 0.424275 (-0.356366) | 0.011054 / 0.007607 (0.003447) | 0.534748 / 0.226044 (0.308703) | 5.351457 / 2.268929 (3.082529) | 2.517368 / 55.444624 (-52.927256) | 2.182762 / 6.876477 (-4.693715) | 2.238205 / 2.142072 (0.096133) | 0.672962 / 4.805227 (-4.132265) | 0.136098 / 6.500664 (-6.364566) | 0.066534 / 0.075469 (-0.008935) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281241 / 1.841788 (-0.560547) | 13.872881 / 8.074308 (5.798573) | 13.161023 / 10.191392 (2.969631) | 0.130011 / 0.680424 (-0.550412) | 0.016759 / 0.534201 (-0.517442) | 0.359802 / 0.579283 (-0.219481) | 0.392577 / 0.434364 (-0.041787) | 0.427742 / 0.540337 (-0.112595) | 0.522241 / 1.386936 (-0.864695) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005985 / 0.011353 (-0.005368) | 0.003705 / 0.011008 (-0.007304) | 0.077699 / 0.038508 (0.039191) | 0.035686 / 0.023109 (0.012577) | 0.420356 / 0.275898 (0.144458) | 0.476753 / 0.323480 (0.153273) | 0.003510 / 0.007986 (-0.004475) | 0.002807 / 0.004328 (-0.001521) | 0.077151 / 0.004250 (0.072901) | 0.046420 / 0.037052 (0.009368) | 0.391781 / 0.258489 (0.133292) | 0.461128 / 0.293841 (0.167287) | 0.027847 / 0.128546 (-0.100699) | 0.008322 / 0.075646 (-0.067324) | 0.082768 / 0.419271 (-0.336503) | 0.042629 / 0.043533 (-0.000904) | 0.405745 / 0.255139 (0.150606) | 0.430797 / 0.283200 (0.147598) | 0.019832 / 0.141683 (-0.121851) | 1.556208 / 1.452155 (0.104054) | 1.612166 / 1.492716 (0.119450) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230633 / 0.018006 (0.212626) | 0.401667 / 0.000490 (0.401178) | 0.000776 / 0.000200 (0.000576) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024959 / 0.037411 (-0.012452) | 0.100560 / 0.014526 (0.086034) | 0.109175 / 0.176557 (-0.067382) | 0.159919 / 0.737135 (-0.577217) | 0.112810 / 0.296338 (-0.183528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460601 / 0.215209 (0.245392) | 4.620039 / 2.077655 (2.542385) | 2.257900 / 1.504120 (0.753780) | 2.039192 / 1.541195 (0.497997) | 2.064451 / 1.468490 (0.595961) | 0.557887 / 4.584777 (-4.026890) | 3.356100 / 3.745712 (-0.389612) | 1.703578 / 5.269862 (-3.566284) | 1.024984 / 4.565676 (-3.540693) | 0.067602 / 0.424275 (-0.356673) | 0.011450 / 0.007607 (0.003842) | 0.563230 / 0.226044 (0.337186) | 5.632150 / 2.268929 (3.363221) | 2.698701 / 55.444624 (-52.745924) | 2.363218 / 6.876477 (-4.513259) | 2.363997 / 2.142072 (0.221925) | 0.671260 / 4.805227 (-4.133967) | 0.136166 / 6.500664 (-6.364499) | 0.067094 / 0.075469 (-0.008375) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303030 / 1.841788 (-0.538757) | 14.137277 / 8.074308 (6.062969) | 13.937631 / 10.191392 (3.746239) | 0.162626 / 0.680424 (-0.517798) | 0.016687 / 0.534201 (-0.517514) | 0.363657 / 0.579283 (-0.215626) | 0.392021 / 0.434364 (-0.042343) | 0.427275 / 0.540337 (-0.113062) | 0.512192 / 1.386936 (-0.874744) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42603528d9bd8c3ab287ed0eadc7fa3d1ef4cfd8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005974 / 0.011353 (-0.005378) | 0.003947 / 0.011008 (-0.007061) | 0.098604 / 0.038508 (0.060096) | 0.036947 / 0.023109 (0.013838) | 0.311844 / 0.275898 (0.035946) | 0.375243 / 0.323480 (0.051763) | 0.003453 / 0.007986 (-0.004533) | 0.003834 / 0.004328 (-0.000495) | 0.077943 / 0.004250 (0.073692) | 0.052956 / 0.037052 (0.015904) | 0.320812 / 0.258489 (0.062323) | 0.373963 / 0.293841 (0.080122) | 0.028382 / 0.128546 (-0.100164) | 0.008525 / 0.075646 (-0.067121) | 0.311306 / 0.419271 (-0.107965) | 0.047029 / 0.043533 (0.003496) | 0.309933 / 0.255139 (0.054794) | 0.335114 / 0.283200 (0.051915) | 0.019629 / 0.141683 (-0.122054) | 1.569771 / 1.452155 (0.117617) | 1.585899 / 1.492716 (0.093182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216565 / 0.018006 (0.198559) | 0.426717 / 0.000490 (0.426228) | 0.003609 / 0.000200 (0.003409) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023079 / 0.037411 (-0.014332) | 0.096954 / 0.014526 (0.082428) | 0.105398 / 0.176557 (-0.071158) | 0.165433 / 0.737135 (-0.571703) | 0.109703 / 0.296338 (-0.186636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456227 / 0.215209 (0.241018) | 4.529857 / 2.077655 (2.452202) | 2.214054 / 1.504120 (0.709934) | 2.029716 / 1.541195 (0.488521) | 2.081175 / 1.468490 (0.612685) | 0.563642 / 4.584777 (-4.021135) | 3.355393 / 3.745712 (-0.390320) | 1.765938 / 5.269862 (-3.503924) | 1.039062 / 4.565676 (-3.526615) | 0.067952 / 0.424275 (-0.356323) | 0.011044 / 0.007607 (0.003437) | 0.556935 / 0.226044 (0.330890) | 5.588167 / 2.268929 (3.319239) | 2.667217 / 55.444624 (-52.777407) | 2.337383 / 6.876477 (-4.539094) | 2.429590 / 2.142072 (0.287517) | 0.676972 / 4.805227 (-4.128256) | 0.135782 / 6.500664 (-6.364882) | 0.066323 / 0.075469 (-0.009146) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237358 / 1.841788 (-0.604429) | 13.910492 / 8.074308 (5.836184) | 13.227275 / 10.191392 (3.035883) | 0.146857 / 0.680424 (-0.533567) | 0.016991 / 0.534201 (-0.517210) | 0.363637 / 0.579283 (-0.215646) | 0.392462 / 0.434364 (-0.041902) | 0.450009 / 0.540337 (-0.090329) | 0.536077 / 1.386936 (-0.850859) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006067 / 0.011353 (-0.005286) | 0.003851 / 0.011008 (-0.007158) | 0.078462 / 0.038508 (0.039954) | 0.036221 / 0.023109 (0.013112) | 0.389195 / 0.275898 (0.113297) | 0.428710 / 0.323480 (0.105230) | 0.004645 / 0.007986 (-0.003341) | 0.002973 / 0.004328 (-0.001355) | 0.078299 / 0.004250 (0.074048) | 0.047076 / 0.037052 (0.010024) | 0.375673 / 0.258489 (0.117184) | 0.432352 / 0.293841 (0.138511) | 0.028212 / 0.128546 (-0.100334) | 0.008475 / 0.075646 (-0.067172) | 0.083902 / 0.419271 (-0.335369) | 0.046699 / 0.043533 (0.003166) | 0.364502 / 0.255139 (0.109363) | 0.389792 / 0.283200 (0.106592) | 0.025266 / 0.141683 (-0.116417) | 1.517458 / 1.452155 (0.065303) | 1.543634 / 1.492716 (0.050918) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236479 / 0.018006 (0.218472) | 0.411528 / 0.000490 (0.411038) | 0.005213 / 0.000200 (0.005013) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025764 / 0.037411 (-0.011647) | 0.103174 / 0.014526 (0.088648) | 0.110609 / 0.176557 (-0.065948) | 0.164630 / 0.737135 (-0.572506) | 0.114863 / 0.296338 (-0.181475) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457155 / 0.215209 (0.241946) | 4.550675 / 2.077655 (2.473021) | 2.350473 / 1.504120 (0.846353) | 2.204919 / 1.541195 (0.663724) | 2.076724 / 1.468490 (0.608234) | 0.563107 / 4.584777 (-4.021670) | 3.390669 / 3.745712 (-0.355043) | 1.741111 / 5.269862 (-3.528751) | 1.033268 / 4.565676 (-3.532408) | 0.068400 / 0.424275 (-0.355875) | 0.011607 / 0.007607 (0.004000) | 0.561944 / 0.226044 (0.335900) | 5.620224 / 2.268929 (3.351296) | 2.705241 / 55.444624 (-52.739384) | 2.344520 / 6.876477 (-4.531957) | 2.386119 / 2.142072 (0.244046) | 0.681583 / 4.805227 (-4.123644) | 0.137272 / 6.500664 (-6.363392) | 0.069217 / 0.075469 (-0.006252) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322690 / 1.841788 (-0.519098) | 14.464953 / 8.074308 (6.390645) | 14.269350 / 10.191392 (4.077958) | 0.158879 / 0.680424 (-0.521545) | 0.016722 / 0.534201 (-0.517479) | 0.360299 / 0.579283 (-0.218984) | 0.391609 / 0.434364 (-0.042755) | 0.420507 / 0.540337 (-0.119831) | 0.512822 / 1.386936 (-0.874114) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca68191900d97b29abb3c2c4ba0502fe30d137d1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007106 / 0.011353 (-0.004247) | 0.005224 / 0.011008 (-0.005784) | 0.127563 / 0.038508 (0.089055) | 0.055067 / 0.023109 (0.031958) | 0.418660 / 0.275898 (0.142761) | 0.487891 / 0.323480 (0.164411) | 0.005712 / 0.007986 (-0.002274) | 0.004585 / 0.004328 (0.000256) | 0.090994 / 0.004250 (0.086743) | 0.071837 / 0.037052 (0.034784) | 0.446957 / 0.258489 (0.188468) | 0.475966 / 0.293841 (0.182125) | 0.038062 / 0.128546 (-0.090484) | 0.010056 / 0.075646 (-0.065590) | 0.406796 / 0.419271 (-0.012475) | 0.066542 / 0.043533 (0.023009) | 0.413676 / 0.255139 (0.158537) | 0.448624 / 0.283200 (0.165424) | 0.030332 / 0.141683 (-0.111351) | 1.895307 / 1.452155 (0.443152) | 1.904411 / 1.492716 (0.411694) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221246 / 0.018006 (0.203240) | 0.461288 / 0.000490 (0.460799) | 0.005957 / 0.000200 (0.005757) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029255 / 0.037411 (-0.008156) | 0.131299 / 0.014526 (0.116773) | 0.135814 / 0.176557 (-0.040742) | 0.201342 / 0.737135 (-0.535793) | 0.141748 / 0.296338 (-0.154591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463936 / 0.215209 (0.248727) | 4.709621 / 2.077655 (2.631966) | 2.093844 / 1.504120 (0.589724) | 1.897963 / 1.541195 (0.356768) | 1.927865 / 1.468490 (0.459375) | 0.610879 / 4.584777 (-3.973898) | 4.481370 / 3.745712 (0.735658) | 2.112235 / 5.269862 (-3.157627) | 1.203349 / 4.565676 (-3.362327) | 0.074828 / 0.424275 (-0.349447) | 0.013121 / 0.007607 (0.005514) | 0.580894 / 0.226044 (0.354849) | 5.801872 / 2.268929 (3.532943) | 2.579950 / 55.444624 (-52.864674) | 2.251569 / 6.876477 (-4.624908) | 2.421305 / 2.142072 (0.279232) | 0.760938 / 4.805227 (-4.044289) | 0.169554 / 6.500664 (-6.331110) | 0.077499 / 0.075469 (0.002030) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.410419 / 1.841788 (-0.431368) | 17.442331 / 8.074308 (9.368023) | 15.782183 / 10.191392 (5.590791) | 0.180649 / 0.680424 (-0.499775) | 0.021790 / 0.534201 (-0.512411) | 0.511040 / 0.579283 (-0.068243) | 0.510472 / 0.434364 (0.076108) | 0.607141 / 0.540337 (0.066804) | 0.724794 / 1.386936 (-0.662142) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007280 / 0.011353 (-0.004073) | 0.004712 / 0.011008 (-0.006296) | 0.089225 / 0.038508 (0.050717) | 0.053157 / 0.023109 (0.030048) | 0.431949 / 0.275898 (0.156051) | 0.478128 / 0.323480 (0.154648) | 0.006181 / 0.007986 (-0.001804) | 0.003387 / 0.004328 (-0.000941) | 0.083741 / 0.004250 (0.079490) | 0.071610 / 0.037052 (0.034557) | 0.414698 / 0.258489 (0.156209) | 0.484422 / 0.293841 (0.190581) | 0.034988 / 0.128546 (-0.093558) | 0.009831 / 0.075646 (-0.065816) | 0.089644 / 0.419271 (-0.329628) | 0.057053 / 0.043533 (0.013520) | 0.413144 / 0.255139 (0.158005) | 0.445464 / 0.283200 (0.162264) | 0.026109 / 0.141683 (-0.115574) | 1.842899 / 1.452155 (0.390745) | 1.923774 / 1.492716 (0.431057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245051 / 0.018006 (0.227045) | 0.460444 / 0.000490 (0.459954) | 0.000444 / 0.000200 (0.000244) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034835 / 0.037411 (-0.002577) | 0.130078 / 0.014526 (0.115553) | 0.147012 / 0.176557 (-0.029544) | 0.203097 / 0.737135 (-0.534038) | 0.149636 / 0.296338 (-0.146702) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521664 / 0.215209 (0.306455) | 5.283865 / 2.077655 (3.206210) | 2.456701 / 1.504120 (0.952581) | 2.266059 / 1.541195 (0.724864) | 2.295387 / 1.468490 (0.826897) | 0.613200 / 4.584777 (-3.971577) | 4.526107 / 3.745712 (0.780394) | 2.047327 / 5.269862 (-3.222535) | 1.261063 / 4.565676 (-3.304614) | 0.070402 / 0.424275 (-0.353873) | 0.014128 / 0.007607 (0.006521) | 0.620929 / 0.226044 (0.394884) | 6.109127 / 2.268929 (3.840198) | 3.081406 / 55.444624 (-52.363218) | 2.658224 / 6.876477 (-4.218253) | 2.671974 / 2.142072 (0.529902) | 0.744081 / 4.805227 (-4.061146) | 0.161498 / 6.500664 (-6.339166) | 0.075148 / 0.075469 (-0.000321) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585640 / 1.841788 (-0.256148) | 17.884321 / 8.074308 (9.810013) | 15.938937 / 10.191392 (5.747545) | 0.220818 / 0.680424 (-0.459605) | 0.021452 / 0.534201 (-0.512749) | 0.499747 / 0.579283 (-0.079536) | 0.512318 / 0.434364 (0.077954) | 0.562853 / 0.540337 (0.022515) | 0.678512 / 1.386936 (-0.708424) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa50937d82256827aee3dbd749c7a23555e05e38 \"CML watermark\")\n" ]

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[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5992). All of your documentation changes will be reflected on that endpoint." ]

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[ "in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)", "I imagine that we should stop supporting them, and help the user fix them?", "See a report where the datasets server fails: https://huggingface.co/datasets/poloclub/diffusiondb/discussions/2#6374ff55b93cbdf65675f564\r\n\r\nThe config name is `random_10k [2m]`!" ]

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[ "Unfortunately, I can't reproduce the error. What does the following code return for you?\r\n```python\r\nimport requests\r\nfrom huggingface_hub import hf_hub_url\r\nr = requests.get(hf_hub_url(\"codeparrot/codeparrot-clean-train\", \"dataset_infos.json\", repo_type=\"dataset\"))\r\n```\r\n\r\nAlso, can you provide more info about your network (region, proxies, etc.)?" ]

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[ "Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.", "In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure blob or hdfs which may automatically download the shard from background)", "But `load_dataset` doesn't support caching (and reading) Arrow datasets from remote storage. \r\n\r\n`load_datset_builder` + `download_and_prepare` is not equal to `load_dataset`. The latter has one more step, `builder.as_dataset`, that memory-maps Arrow files, which only works for local files.", "Thanks. So if I want to use `IterableDataset` and control the size of single arrow file, how should I organize the data loader? Maybe `load_dataset_build` + `download_and_prepare` + `builder.as_dataset` + `dataset.to_iterable_dataset`?", "Yes, this should work.\r\n\r\nI think we can expose `max_shard_size` in `load_dataset`, so feel free to open a PR." ]

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[ "@lhoestq would you be able to review this please and also approve the workflow?", "Sounds good to me :) feel free to run `make style` to apply code formatting", "_The documentation is not available anymore as the PR was closed or merged._", "cool ! I think we can merge once all comments have been addressed", "@lhoestq I just addressed the comments and I think we can move ahead with this! \r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007734 / 0.011353 (-0.003619) | 0.004608 / 0.011008 (-0.006400) | 0.094466 / 0.038508 (0.055958) | 0.086477 / 0.023109 (0.063368) | 0.410311 / 0.275898 (0.134413) | 0.455560 / 0.323480 (0.132080) | 0.006112 / 0.007986 (-0.001874) | 0.003845 / 0.004328 (-0.000483) | 0.072506 / 0.004250 (0.068256) | 0.066721 / 0.037052 (0.029669) | 0.409967 / 0.258489 (0.151478) | 0.460480 / 0.293841 (0.166639) | 0.036700 / 0.128546 (-0.091847) | 0.009854 / 0.075646 (-0.065792) | 0.320936 / 0.419271 (-0.098335) | 0.061002 / 0.043533 (0.017469) | 0.413963 / 0.255139 (0.158824) | 0.426787 / 0.283200 (0.143588) | 0.029182 / 0.141683 (-0.112501) | 1.685136 / 1.452155 (0.232981) | 1.754590 / 1.492716 (0.261873) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222698 / 0.018006 (0.204692) | 0.505929 / 0.000490 (0.505440) | 0.005291 / 0.000200 (0.005091) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032527 / 0.037411 (-0.004884) | 0.094842 / 0.014526 (0.080317) | 0.110138 / 0.176557 (-0.066418) | 0.193786 / 0.737135 (-0.543349) | 0.112593 / 0.296338 (-0.183745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441671 / 0.215209 (0.226461) | 4.392961 / 2.077655 (2.315306) | 2.161111 / 1.504120 (0.656991) | 1.967080 / 1.541195 (0.425885) | 2.065411 / 1.468490 (0.596920) | 0.561080 / 4.584777 (-4.023697) | 4.159612 / 3.745712 (0.413900) | 6.435248 / 5.269862 (1.165386) | 3.732338 / 4.565676 (-0.833339) | 0.066156 / 0.424275 (-0.358119) | 0.008030 / 0.007607 (0.000423) | 0.532182 / 0.226044 (0.306137) | 5.315142 / 2.268929 (3.046213) | 2.680157 / 55.444624 (-52.764467) | 2.303799 / 6.876477 (-4.572677) | 2.530911 / 2.142072 (0.388838) | 0.669504 / 4.805227 (-4.135723) | 0.151940 / 6.500664 (-6.348724) | 0.066999 / 0.075469 (-0.008470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.424275 / 1.841788 (-0.417513) | 21.550742 / 8.074308 (13.476434) | 16.031414 / 10.191392 (5.840022) | 0.194681 / 0.680424 (-0.485743) | 0.020389 / 0.534201 (-0.513812) | 0.429808 / 0.579283 (-0.149475) | 0.457503 / 0.434364 (0.023139) | 0.511522 / 0.540337 (-0.028816) | 0.682621 / 1.386936 (-0.704315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007519 / 0.011353 (-0.003834) | 0.004445 / 0.011008 (-0.006563) | 0.071946 / 0.038508 (0.033438) | 0.082982 / 0.023109 (0.059873) | 0.459938 / 0.275898 (0.184040) | 0.504875 / 0.323480 (0.181395) | 0.005805 / 0.007986 (-0.002181) | 0.003740 / 0.004328 (-0.000589) | 0.071998 / 0.004250 (0.067747) | 0.062580 / 0.037052 (0.025527) | 0.462263 / 0.258489 (0.203774) | 0.506355 / 0.293841 (0.212514) | 0.036321 / 0.128546 (-0.092225) | 0.009830 / 0.075646 (-0.065816) | 0.079810 / 0.419271 (-0.339461) | 0.055291 / 0.043533 (0.011758) | 0.464093 / 0.255139 (0.208954) | 0.481109 / 0.283200 (0.197910) | 0.026909 / 0.141683 (-0.114774) | 1.652538 / 1.452155 (0.200383) | 1.750713 / 1.492716 (0.257997) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267552 / 0.018006 (0.249546) | 0.502021 / 0.000490 (0.501531) | 0.001635 / 0.000200 (0.001435) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033747 / 0.037411 (-0.003665) | 0.104242 / 0.014526 (0.089716) | 0.113829 / 0.176557 (-0.062728) | 0.176242 / 0.737135 (-0.560893) | 0.117002 / 0.296338 (-0.179336) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476731 / 0.215209 (0.261522) | 4.727054 / 2.077655 (2.649399) | 2.589396 / 1.504120 (1.085276) | 2.511180 / 1.541195 (0.969985) | 2.634122 / 1.468490 (1.165632) | 0.563840 / 4.584777 (-4.020937) | 4.140212 / 3.745712 (0.394500) | 6.188789 / 5.269862 (0.918928) | 3.716897 / 4.565676 (-0.848780) | 0.065823 / 0.424275 (-0.358452) | 0.007705 / 0.007607 (0.000098) | 0.566580 / 0.226044 (0.340535) | 5.653306 / 2.268929 (3.384377) | 3.028756 / 55.444624 (-52.415868) | 2.592319 / 6.876477 (-4.284158) | 2.614250 / 2.142072 (0.472178) | 0.667135 / 4.805227 (-4.138093) | 0.153455 / 6.500664 (-6.347209) | 0.069321 / 0.075469 (-0.006148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541978 / 1.841788 (-0.299810) | 21.747360 / 8.074308 (13.673052) | 15.963657 / 10.191392 (5.772265) | 0.192843 / 0.680424 (-0.487581) | 0.020702 / 0.534201 (-0.513499) | 0.433620 / 0.579283 (-0.145663) | 0.467327 / 0.434364 (0.032963) | 0.507398 / 0.540337 (-0.032940) | 0.692797 / 1.386936 (-0.694140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#396cf9419d12e3150e2051793b10f2c813780a90 \"CML watermark\")\n" ]

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[ "This is a known issue: https://github.com/huggingface/datasets/issues/3847.\r\n\r\nFixing this requires significant work - rewriting the `tokenizers` lib to make them immutable.\r\n\r\nThe current solution is to pass `cache_file_name` to `map` to use that file for caching or calling a tokenizer before `map` (with the same set of parameters as the ones in the map transform)", "Closing since this is a duplicate" ]

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[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example." ]

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[ "This wasn’t working for me a bit earlier, but it looks to be back up now", "We had a minor issue updating the docs after the latest release. It should work now :)." ]

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[ "I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625", "From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration.", "This is too specific and unrelated to `datasets`, so this shouldn't be fixed here." ]

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[ "Can you try again? Maybe there was a minor outage.", "Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ", "we fixed something on the server side, glad it's fixed now" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5979). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008087 / 0.011353 (-0.003266) | 0.004691 / 0.011008 (-0.006317) | 0.121545 / 0.038508 (0.083037) | 0.057436 / 0.023109 (0.034326) | 0.368864 / 0.275898 (0.092966) | 0.457199 / 0.323480 (0.133719) | 0.006745 / 0.007986 (-0.001241) | 0.003689 / 0.004328 (-0.000640) | 0.090480 / 0.004250 (0.086229) | 0.071368 / 0.037052 (0.034316) | 0.372788 / 0.258489 (0.114299) | 0.429894 / 0.293841 (0.136053) | 0.037544 / 0.128546 (-0.091002) | 0.010142 / 0.075646 (-0.065505) | 0.420467 / 0.419271 (0.001196) | 0.064359 / 0.043533 (0.020826) | 0.370345 / 0.255139 (0.115206) | 0.405220 / 0.283200 (0.122020) | 0.028410 / 0.141683 (-0.113273) | 1.824845 / 1.452155 (0.372690) | 1.888109 / 1.492716 (0.395392) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234585 / 0.018006 (0.216578) | 0.499965 / 0.000490 (0.499476) | 0.000461 / 0.000200 (0.000261) | 0.000064 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032294 / 0.037411 (-0.005117) | 0.131769 / 0.014526 (0.117243) | 0.146472 / 0.176557 (-0.030085) | 0.210035 / 0.737135 (-0.527100) | 0.145600 / 0.296338 (-0.150739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507455 / 0.215209 (0.292246) | 5.080090 / 2.077655 (3.002435) | 2.506104 / 1.504120 (1.001984) | 2.297655 / 1.541195 (0.756460) | 2.324920 / 1.468490 (0.856430) | 0.645003 / 4.584777 (-3.939774) | 4.677856 / 3.745712 (0.932144) | 2.254179 / 5.269862 (-3.015683) | 1.280663 / 4.565676 (-3.285013) | 0.078809 / 0.424275 (-0.345466) | 0.014059 / 0.007607 (0.006452) | 0.628053 / 0.226044 (0.402009) | 6.327289 / 2.268929 (4.058360) | 2.957918 / 55.444624 (-52.486706) | 2.571568 / 6.876477 (-4.304909) | 2.708766 / 2.142072 (0.566694) | 0.772868 / 4.805227 (-4.032360) | 0.164835 / 6.500664 (-6.335829) | 0.075334 / 0.075469 (-0.000135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471930 / 1.841788 (-0.369858) | 17.917340 / 8.074308 (9.843032) | 15.719327 / 10.191392 (5.527935) | 0.191999 / 0.680424 (-0.488424) | 0.022464 / 0.534201 (-0.511737) | 0.511038 / 0.579283 (-0.068245) | 0.512050 / 0.434364 (0.077686) | 0.608711 / 0.540337 (0.068373) | 0.749660 / 1.386936 (-0.637276) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008028 / 0.011353 (-0.003325) | 0.004908 / 0.011008 (-0.006100) | 0.092294 / 0.038508 (0.053786) | 0.053051 / 0.023109 (0.029942) | 0.453862 / 0.275898 (0.177964) | 0.512548 / 0.323480 (0.189068) | 0.004817 / 0.007986 (-0.003168) | 0.005330 / 0.004328 (0.001002) | 0.095600 / 0.004250 (0.091350) | 0.068763 / 0.037052 (0.031710) | 0.453654 / 0.258489 (0.195165) | 0.504995 / 0.293841 (0.211154) | 0.038123 / 0.128546 (-0.090423) | 0.010650 / 0.075646 (-0.064996) | 0.102854 / 0.419271 (-0.316417) | 0.062973 / 0.043533 (0.019440) | 0.430420 / 0.255139 (0.175281) | 0.465448 / 0.283200 (0.182248) | 0.029736 / 0.141683 (-0.111947) | 1.844225 / 1.452155 (0.392070) | 1.934685 / 1.492716 (0.441968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227797 / 0.018006 (0.209791) | 0.467868 / 0.000490 (0.467378) | 0.004531 / 0.000200 (0.004331) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035632 / 0.037411 (-0.001780) | 0.145943 / 0.014526 (0.131417) | 0.151944 / 0.176557 (-0.024613) | 0.220519 / 0.737135 (-0.516616) | 0.159732 / 0.296338 (-0.136606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520641 / 0.215209 (0.305432) | 5.184740 / 2.077655 (3.107086) | 2.538751 / 1.504120 (1.034631) | 2.316571 / 1.541195 (0.775377) | 2.387898 / 1.468490 (0.919408) | 0.614515 / 4.584777 (-3.970262) | 4.573142 / 3.745712 (0.827430) | 4.657052 / 5.269862 (-0.612809) | 2.159664 / 4.565676 (-2.406013) | 0.079713 / 0.424275 (-0.344562) | 0.014462 / 0.007607 (0.006855) | 0.656611 / 0.226044 (0.430566) | 6.481630 / 2.268929 (4.212702) | 3.135047 / 55.444624 (-52.309577) | 2.757502 / 6.876477 (-4.118975) | 2.851488 / 2.142072 (0.709415) | 0.790795 / 4.805227 (-4.014432) | 0.172358 / 6.500664 (-6.328306) | 0.080255 / 0.075469 (0.004786) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.571391 / 1.841788 (-0.270396) | 19.025224 / 8.074308 (10.950916) | 17.079230 / 10.191392 (6.887838) | 0.172823 / 0.680424 (-0.507601) | 0.021845 / 0.534201 (-0.512356) | 0.522286 / 0.579283 (-0.056998) | 0.510406 / 0.434364 (0.076042) | 0.604830 / 0.540337 (0.064493) | 0.735466 / 1.386936 (-0.651471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4084609bdc40d173d1daa74ad2fe98f3ead72f8e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010025 / 0.011353 (-0.001328) | 0.005699 / 0.011008 (-0.005310) | 0.134194 / 0.038508 (0.095686) | 0.056154 / 0.023109 (0.033045) | 0.470091 / 0.275898 (0.194193) | 0.539225 / 0.323480 (0.215745) | 0.006659 / 0.007986 (-0.001326) | 0.004468 / 0.004328 (0.000140) | 0.110040 / 0.004250 (0.105790) | 0.074172 / 0.037052 (0.037119) | 0.497450 / 0.258489 (0.238961) | 0.535048 / 0.293841 (0.241207) | 0.051195 / 0.128546 (-0.077352) | 0.014926 / 0.075646 (-0.060721) | 0.461334 / 0.419271 (0.042062) | 0.073773 / 0.043533 (0.030240) | 0.450741 / 0.255139 (0.195602) | 0.474853 / 0.283200 (0.191653) | 0.036372 / 0.141683 (-0.105311) | 1.982873 / 1.452155 (0.530719) | 1.989912 / 1.492716 (0.497196) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287817 / 0.018006 (0.269811) | 0.613415 / 0.000490 (0.612926) | 0.007082 / 0.000200 (0.006882) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031119 / 0.037411 (-0.006292) | 0.129886 / 0.014526 (0.115361) | 0.143492 / 0.176557 (-0.033065) | 0.208536 / 0.737135 (-0.528600) | 0.147081 / 0.296338 (-0.149257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668312 / 0.215209 (0.453103) | 6.568609 / 2.077655 (4.490955) | 2.708788 / 1.504120 (1.204668) | 2.366737 / 1.541195 (0.825542) | 2.392598 / 1.468490 (0.924108) | 0.967582 / 4.584777 (-3.617195) | 5.582743 / 3.745712 (1.837031) | 3.021607 / 5.269862 (-2.248255) | 1.866402 / 4.565676 (-2.699275) | 0.115998 / 0.424275 (-0.308277) | 0.015571 / 0.007607 (0.007964) | 0.820069 / 0.226044 (0.594025) | 8.229725 / 2.268929 (5.960797) | 3.437068 / 55.444624 (-52.007557) | 2.902312 / 6.876477 (-3.974164) | 3.025874 / 2.142072 (0.883802) | 1.230359 / 4.805227 (-3.574868) | 0.237341 / 6.500664 (-6.263323) | 0.089923 / 0.075469 (0.014453) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670970 / 1.841788 (-0.170818) | 19.667167 / 8.074308 (11.592859) | 21.624423 / 10.191392 (11.433031) | 0.231683 / 0.680424 (-0.448741) | 0.029145 / 0.534201 (-0.505056) | 0.543441 / 0.579283 (-0.035842) | 0.617510 / 0.434364 (0.183146) | 0.612662 / 0.540337 (0.072324) | 0.790589 / 1.386936 (-0.596347) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010324 / 0.011353 (-0.001029) | 0.005339 / 0.011008 (-0.005669) | 0.104762 / 0.038508 (0.066254) | 0.052631 / 0.023109 (0.029522) | 0.485864 / 0.275898 (0.209966) | 0.595768 / 0.323480 (0.272288) | 0.007417 / 0.007986 (-0.000569) | 0.005229 / 0.004328 (0.000900) | 0.100775 / 0.004250 (0.096524) | 0.067144 / 0.037052 (0.030092) | 0.522269 / 0.258489 (0.263780) | 0.592597 / 0.293841 (0.298756) | 0.051101 / 0.128546 (-0.077446) | 0.015277 / 0.075646 (-0.060369) | 0.115530 / 0.419271 (-0.303741) | 0.071922 / 0.043533 (0.028390) | 0.490208 / 0.255139 (0.235069) | 0.578936 / 0.283200 (0.295736) | 0.040382 / 0.141683 (-0.101301) | 1.986059 / 1.452155 (0.533904) | 2.040600 / 1.492716 (0.547883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300399 / 0.018006 (0.282393) | 0.624702 / 0.000490 (0.624212) | 0.004908 / 0.000200 (0.004708) | 0.000155 / 0.000054 (0.000100) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038031 / 0.037411 (0.000619) | 0.140353 / 0.014526 (0.125828) | 0.152600 / 0.176557 (-0.023956) | 0.219165 / 0.737135 (-0.517970) | 0.154232 / 0.296338 (-0.142106) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.698855 / 0.215209 (0.483646) | 7.125543 / 2.077655 (5.047889) | 3.251222 / 1.504120 (1.747102) | 2.953404 / 1.541195 (1.412209) | 3.051108 / 1.468490 (1.582618) | 0.962068 / 4.584777 (-3.622709) | 5.789579 / 3.745712 (2.043867) | 5.193271 / 5.269862 (-0.076591) | 2.757886 / 4.565676 (-1.807790) | 0.111865 / 0.424275 (-0.312410) | 0.014684 / 0.007607 (0.007077) | 0.875967 / 0.226044 (0.649923) | 8.818359 / 2.268929 (6.549430) | 4.165216 / 55.444624 (-51.279408) | 3.372059 / 6.876477 (-3.504418) | 3.486886 / 2.142072 (1.344813) | 1.232276 / 4.805227 (-3.572951) | 0.238967 / 6.500664 (-6.261697) | 0.091584 / 0.075469 (0.016115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.850755 / 1.841788 (0.008968) | 20.058756 / 8.074308 (11.984448) | 23.761271 / 10.191392 (13.569879) | 0.231826 / 0.680424 (-0.448598) | 0.030119 / 0.534201 (-0.504082) | 0.532614 / 0.579283 (-0.046669) | 0.628968 / 0.434364 (0.194604) | 0.628403 / 0.540337 (0.088066) | 0.745648 / 1.386936 (-0.641288) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8a797cc92e860c8d0df71e0aa826f4d2690713e \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006173 / 0.011353 (-0.005180) | 0.003773 / 0.011008 (-0.007235) | 0.099499 / 0.038508 (0.060991) | 0.037918 / 0.023109 (0.014809) | 0.321329 / 0.275898 (0.045431) | 0.379739 / 0.323480 (0.056259) | 0.004664 / 0.007986 (-0.003322) | 0.002943 / 0.004328 (-0.001385) | 0.077759 / 0.004250 (0.073509) | 0.055271 / 0.037052 (0.018219) | 0.329428 / 0.258489 (0.070939) | 0.378731 / 0.293841 (0.084890) | 0.027737 / 0.128546 (-0.100810) | 0.008566 / 0.075646 (-0.067081) | 0.313220 / 0.419271 (-0.106052) | 0.047101 / 0.043533 (0.003568) | 0.316211 / 0.255139 (0.061072) | 0.341826 / 0.283200 (0.058626) | 0.020838 / 0.141683 (-0.120845) | 1.550064 / 1.452155 (0.097909) | 1.706518 / 1.492716 (0.213801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203093 / 0.018006 (0.185087) | 0.425345 / 0.000490 (0.424856) | 0.004800 / 0.000200 (0.004600) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024590 / 0.037411 (-0.012821) | 0.098115 / 0.014526 (0.083589) | 0.108274 / 0.176557 (-0.068282) | 0.170804 / 0.737135 (-0.566332) | 0.110560 / 0.296338 (-0.185778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425251 / 0.215209 (0.210042) | 4.239075 / 2.077655 (2.161421) | 1.955601 / 1.504120 (0.451481) | 1.774796 / 1.541195 (0.233602) | 1.826641 / 1.468490 (0.358150) | 0.558777 / 4.584777 (-4.026000) | 3.361697 / 3.745712 (-0.384015) | 1.764468 / 5.269862 (-3.505394) | 1.032280 / 4.565676 (-3.533396) | 0.067872 / 0.424275 (-0.356403) | 0.010998 / 0.007607 (0.003391) | 0.525682 / 0.226044 (0.299637) | 5.254356 / 2.268929 (2.985427) | 2.384332 / 55.444624 (-53.060292) | 2.045578 / 6.876477 (-4.830898) | 2.170914 / 2.142072 (0.028841) | 0.674782 / 4.805227 (-4.130445) | 0.135351 / 6.500664 (-6.365314) | 0.066591 / 0.075469 (-0.008878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209181 / 1.841788 (-0.632606) | 14.044518 / 8.074308 (5.970210) | 13.184705 / 10.191392 (2.993313) | 0.130836 / 0.680424 (-0.549588) | 0.016582 / 0.534201 (-0.517619) | 0.360005 / 0.579283 (-0.219279) | 0.379519 / 0.434364 (-0.054845) | 0.422174 / 0.540337 (-0.118164) | 0.515546 / 1.386936 (-0.871390) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006293 / 0.011353 (-0.005060) | 0.003784 / 0.011008 (-0.007224) | 0.079248 / 0.038508 (0.040739) | 0.038452 / 0.023109 (0.015343) | 0.444727 / 0.275898 (0.168829) | 0.500535 / 0.323480 (0.177055) | 0.003455 / 0.007986 (-0.004531) | 0.002873 / 0.004328 (-0.001455) | 0.077439 / 0.004250 (0.073189) | 0.047855 / 0.037052 (0.010803) | 0.448049 / 0.258489 (0.189560) | 0.509517 / 0.293841 (0.215676) | 0.028359 / 0.128546 (-0.100188) | 0.008503 / 0.075646 (-0.067143) | 0.084961 / 0.419271 (-0.334310) | 0.042880 / 0.043533 (-0.000653) | 0.436628 / 0.255139 (0.181489) | 0.456574 / 0.283200 (0.173375) | 0.019539 / 0.141683 (-0.122144) | 1.561273 / 1.452155 (0.109118) | 1.572018 / 1.492716 (0.079301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230250 / 0.018006 (0.212244) | 0.415189 / 0.000490 (0.414700) | 0.003213 / 0.000200 (0.003013) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025541 / 0.037411 (-0.011871) | 0.102326 / 0.014526 (0.087800) | 0.110258 / 0.176557 (-0.066298) | 0.162488 / 0.737135 (-0.574647) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457936 / 0.215209 (0.242727) | 4.581503 / 2.077655 (2.503848) | 2.237659 / 1.504120 (0.733540) | 2.029960 / 1.541195 (0.488765) | 2.082911 / 1.468490 (0.614421) | 0.556485 / 4.584777 (-4.028292) | 3.384418 / 3.745712 (-0.361295) | 1.748809 / 5.269862 (-3.521053) | 1.034759 / 4.565676 (-3.530917) | 0.067500 / 0.424275 (-0.356776) | 0.011425 / 0.007607 (0.003818) | 0.561340 / 0.226044 (0.335295) | 5.623629 / 2.268929 (3.354701) | 2.733587 / 55.444624 (-52.711038) | 2.401578 / 6.876477 (-4.474899) | 2.524569 / 2.142072 (0.382496) | 0.673170 / 4.805227 (-4.132057) | 0.136681 / 6.500664 (-6.363983) | 0.068060 / 0.075469 (-0.007409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318651 / 1.841788 (-0.523137) | 14.362123 / 8.074308 (6.287815) | 14.385964 / 10.191392 (4.194572) | 0.149914 / 0.680424 (-0.530510) | 0.016877 / 0.534201 (-0.517324) | 0.358406 / 0.579283 (-0.220877) | 0.394349 / 0.434364 (-0.040015) | 0.422471 / 0.540337 (-0.117866) | 0.513807 / 1.386936 (-0.873129) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006272 / 0.011353 (-0.005080) | 0.003903 / 0.011008 (-0.007105) | 0.100180 / 0.038508 (0.061672) | 0.037799 / 0.023109 (0.014690) | 0.385627 / 0.275898 (0.109729) | 0.446518 / 0.323480 (0.123038) | 0.004811 / 0.007986 (-0.003175) | 0.003032 / 0.004328 (-0.001296) | 0.077063 / 0.004250 (0.072812) | 0.055564 / 0.037052 (0.018512) | 0.397346 / 0.258489 (0.138857) | 0.443242 / 0.293841 (0.149401) | 0.027904 / 0.128546 (-0.100642) | 0.008386 / 0.075646 (-0.067260) | 0.315013 / 0.419271 (-0.104259) | 0.047943 / 0.043533 (0.004410) | 0.378443 / 0.255139 (0.123304) | 0.411472 / 0.283200 (0.128272) | 0.020465 / 0.141683 (-0.121218) | 1.526594 / 1.452155 (0.074439) | 1.547018 / 1.492716 (0.054301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219377 / 0.018006 (0.201370) | 0.430254 / 0.000490 (0.429764) | 0.003218 / 0.000200 (0.003018) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023667 / 0.037411 (-0.013744) | 0.099143 / 0.014526 (0.084617) | 0.106044 / 0.176557 (-0.070513) | 0.166186 / 0.737135 (-0.570949) | 0.108736 / 0.296338 (-0.187603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437971 / 0.215209 (0.222762) | 4.363675 / 2.077655 (2.286021) | 2.011993 / 1.504120 (0.507873) | 1.845189 / 1.541195 (0.303994) | 1.831848 / 1.468490 (0.363358) | 0.562402 / 4.584777 (-4.022375) | 3.365259 / 3.745712 (-0.380453) | 1.781491 / 5.269862 (-3.488371) | 1.023454 / 4.565676 (-3.542223) | 0.067857 / 0.424275 (-0.356418) | 0.011076 / 0.007607 (0.003469) | 0.532267 / 0.226044 (0.306223) | 5.340344 / 2.268929 (3.071415) | 2.388649 / 55.444624 (-53.055976) | 2.055373 / 6.876477 (-4.821104) | 2.205047 / 2.142072 (0.062975) | 0.672909 / 4.805227 (-4.132318) | 0.135244 / 6.500664 (-6.365420) | 0.066184 / 0.075469 (-0.009285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206838 / 1.841788 (-0.634950) | 13.967075 / 8.074308 (5.892767) | 13.143971 / 10.191392 (2.952579) | 0.143991 / 0.680424 (-0.536433) | 0.016673 / 0.534201 (-0.517527) | 0.376180 / 0.579283 (-0.203103) | 0.386550 / 0.434364 (-0.047814) | 0.440590 / 0.540337 (-0.099747) | 0.529974 / 1.386936 (-0.856962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003784 / 0.011008 (-0.007224) | 0.077875 / 0.038508 (0.039367) | 0.038689 / 0.023109 (0.015580) | 0.421684 / 0.275898 (0.145786) | 0.472649 / 0.323480 (0.149169) | 0.003570 / 0.007986 (-0.004415) | 0.004448 / 0.004328 (0.000120) | 0.077867 / 0.004250 (0.073616) | 0.049514 / 0.037052 (0.012462) | 0.375983 / 0.258489 (0.117494) | 0.470632 / 0.293841 (0.176791) | 0.028238 / 0.128546 (-0.100308) | 0.008462 / 0.075646 (-0.067185) | 0.082452 / 0.419271 (-0.336819) | 0.043617 / 0.043533 (0.000084) | 0.400874 / 0.255139 (0.145735) | 0.426191 / 0.283200 (0.142992) | 0.020602 / 0.141683 (-0.121081) | 1.567658 / 1.452155 (0.115504) | 1.572610 / 1.492716 (0.079893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246144 / 0.018006 (0.228138) | 0.419402 / 0.000490 (0.418913) | 0.001691 / 0.000200 (0.001491) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026105 / 0.037411 (-0.011306) | 0.104734 / 0.014526 (0.090208) | 0.110257 / 0.176557 (-0.066300) | 0.161429 / 0.737135 (-0.575706) | 0.114367 / 0.296338 (-0.181972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453352 / 0.215209 (0.238143) | 4.537924 / 2.077655 (2.460269) | 2.196193 / 1.504120 (0.692073) | 2.002087 / 1.541195 (0.460892) | 2.041722 / 1.468490 (0.573231) | 0.561643 / 4.584777 (-4.023134) | 3.449108 / 3.745712 (-0.296605) | 2.862800 / 5.269862 (-2.407062) | 1.387895 / 4.565676 (-3.177782) | 0.068076 / 0.424275 (-0.356199) | 0.011568 / 0.007607 (0.003961) | 0.559279 / 0.226044 (0.333235) | 5.598738 / 2.268929 (3.329809) | 2.676649 / 55.444624 (-52.767975) | 2.334588 / 6.876477 (-4.541889) | 2.376215 / 2.142072 (0.234142) | 0.673109 / 4.805227 (-4.132118) | 0.137587 / 6.500664 (-6.363077) | 0.069131 / 0.075469 (-0.006338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307332 / 1.841788 (-0.534456) | 14.536036 / 8.074308 (6.461728) | 14.173734 / 10.191392 (3.982342) | 0.145143 / 0.680424 (-0.535281) | 0.016662 / 0.534201 (-0.517539) | 0.366901 / 0.579283 (-0.212383) | 0.394498 / 0.434364 (-0.039866) | 0.430546 / 0.540337 (-0.109792) | 0.518950 / 1.386936 (-0.867986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008122 / 0.011353 (-0.003231) | 0.005585 / 0.011008 (-0.005424) | 0.121219 / 0.038508 (0.082711) | 0.047616 / 0.023109 (0.024507) | 0.440576 / 0.275898 (0.164678) | 0.491053 / 0.323480 (0.167573) | 0.004774 / 0.007986 (-0.003211) | 0.006758 / 0.004328 (0.002430) | 0.103852 / 0.004250 (0.099602) | 0.071560 / 0.037052 (0.034508) | 0.463107 / 0.258489 (0.204618) | 0.516904 / 0.293841 (0.223063) | 0.048052 / 0.128546 (-0.080494) | 0.013679 / 0.075646 (-0.061968) | 0.428383 / 0.419271 (0.009112) | 0.069468 / 0.043533 (0.025936) | 0.432593 / 0.255139 (0.177454) | 0.471810 / 0.283200 (0.188611) | 0.037541 / 0.141683 (-0.104142) | 1.823490 / 1.452155 (0.371335) | 1.922558 / 1.492716 (0.429842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252315 / 0.018006 (0.234309) | 0.541757 / 0.000490 (0.541267) | 0.000373 / 0.000200 (0.000173) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030361 / 0.037411 (-0.007050) | 0.125928 / 0.014526 (0.111402) | 0.145102 / 0.176557 (-0.031455) | 0.209798 / 0.737135 (-0.527337) | 0.147349 / 0.296338 (-0.148990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627554 / 0.215209 (0.412345) | 5.917422 / 2.077655 (3.839767) | 2.491083 / 1.504120 (0.986963) | 2.147078 / 1.541195 (0.605883) | 2.167511 / 1.468490 (0.699021) | 0.903061 / 4.584777 (-3.681716) | 5.518537 / 3.745712 (1.772825) | 2.654348 / 5.269862 (-2.615514) | 1.645121 / 4.565676 (-2.920556) | 0.103782 / 0.424275 (-0.320493) | 0.013048 / 0.007607 (0.005441) | 0.756732 / 0.226044 (0.530687) | 7.622873 / 2.268929 (5.353945) | 3.122689 / 55.444624 (-52.321936) | 2.537735 / 6.876477 (-4.338742) | 2.640090 / 2.142072 (0.498018) | 1.128635 / 4.805227 (-3.676593) | 0.228089 / 6.500664 (-6.272575) | 0.086207 / 0.075469 (0.010738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561591 / 1.841788 (-0.280197) | 18.110299 / 8.074308 (10.035991) | 20.718017 / 10.191392 (10.526625) | 0.225741 / 0.680424 (-0.454682) | 0.031738 / 0.534201 (-0.502463) | 0.530789 / 0.579283 (-0.048495) | 0.607364 / 0.434364 (0.173000) | 0.581593 / 0.540337 (0.041256) | 0.726033 / 1.386936 (-0.660903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009323 / 0.011353 (-0.002030) | 0.005360 / 0.011008 (-0.005649) | 0.103608 / 0.038508 (0.065100) | 0.050158 / 0.023109 (0.027049) | 0.499906 / 0.275898 (0.224008) | 0.561005 / 0.323480 (0.237525) | 0.005093 / 0.007986 (-0.002892) | 0.008285 / 0.004328 (0.003956) | 0.103446 / 0.004250 (0.099196) | 0.061478 / 0.037052 (0.024426) | 0.494016 / 0.258489 (0.235527) | 0.537550 / 0.293841 (0.243709) | 0.048829 / 0.128546 (-0.079717) | 0.017032 / 0.075646 (-0.058614) | 0.107748 / 0.419271 (-0.311524) | 0.065607 / 0.043533 (0.022074) | 0.488709 / 0.255139 (0.233570) | 0.512023 / 0.283200 (0.228823) | 0.032067 / 0.141683 (-0.109616) | 1.907585 / 1.452155 (0.455431) | 1.960994 / 1.492716 (0.468278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.018006 (0.260371) | 0.551474 / 0.000490 (0.550985) | 0.006886 / 0.000200 (0.006686) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.135179 / 0.014526 (0.120654) | 0.133703 / 0.176557 (-0.042853) | 0.198923 / 0.737135 (-0.538212) | 0.155108 / 0.296338 (-0.141231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690566 / 0.215209 (0.475357) | 6.789594 / 2.077655 (4.711940) | 2.940668 / 1.504120 (1.436549) | 2.562431 / 1.541195 (1.021236) | 2.554232 / 1.468490 (1.085742) | 0.888470 / 4.584777 (-3.696307) | 5.672318 / 3.745712 (1.926606) | 2.741626 / 5.269862 (-2.528236) | 1.818336 / 4.565676 (-2.747340) | 0.110434 / 0.424275 (-0.313841) | 0.014114 / 0.007607 (0.006507) | 0.830632 / 0.226044 (0.604588) | 8.270787 / 2.268929 (6.001859) | 3.723486 / 55.444624 (-51.721139) | 2.993671 / 6.876477 (-3.882806) | 2.918273 / 2.142072 (0.776201) | 1.105337 / 4.805227 (-3.699891) | 0.222976 / 6.500664 (-6.277688) | 0.085290 / 0.075469 (0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816027 / 1.841788 (-0.025760) | 18.496850 / 8.074308 (10.422541) | 20.457032 / 10.191392 (10.265640) | 0.243533 / 0.680424 (-0.436891) | 0.027044 / 0.534201 (-0.507157) | 0.500752 / 0.579283 (-0.078531) | 0.620963 / 0.434364 (0.186599) | 0.607995 / 0.540337 (0.067658) | 0.722915 / 1.386936 (-0.664021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]

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[ "Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)", "@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiprocess.pool.Pool)` to keep track of the PID changes.", "_The documentation is not available anymore as the PR was closed or merged._", "I managed to raise an error without subclassing Pool with two additions to `iflatmap_unordered`:\r\n\r\n1. at the beggining\r\n```python\r\noriginal_pool = list(pool._pool)\r\n```\r\n\r\n2. in the loop\r\n```python\r\nif any(async_result._pool != original_pool for async_result in async_results) and queue.empty():\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```\r\n\r\nIt's still a fix that only works for `iflatmap_unordered` (so not for map, imap etc) but is maybe simpler that subclassing. It also works for both multiprocessing.Pool and multiprocess.Pool", "@lhoestq sorry for the delay. Busy weeks here. \r\n\r\nI just pushed the change you requested. It looks closer to the original proposal, actually.\r\n\r\nIt seems that `map` actually uses `iflatmap_unordered` ([here](https://github.com/huggingface/datasets/blob/819bb4346434912eb405ce3f3e9f21dc25a2fe85/src/datasets/arrow_dataset.py#L1509)). I think this solution works fine for the `map` method (which is the one being tested by the new `tests/test_arrow_dataset.py::BaseDatasetTest::test_map_crash_subprocess`, right?).", "Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.", "It looks all good to me, feel free to fix code formatting by running `make style` and we can merge :)", "> Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.\r\n\r\nRight, I agree. The best way moving forward is probably not using the buggy `multiprocess.Pool` anymore, and replace it with `concurrent.futures.ProcessPoolExecutor` as much as possible.\r\n\r\nAnyway, I've run `make style` now. Thanks for the support!", "It looks like checking the async_result._pool doesn't always work - sorry about that. We might just go back to your original solution then. Would also be cool to open an issue in `multiprocess` to ask if they have a solution or if they plan to fix this.", "@lhoestq no problem! Reverted to the previous version.\r\n\r\nTBH, given the discussions [in this python issue](https://github.com/python/cpython/issues/66587), I don't think the error in `multiprocess` will be merged upstream any time soon...", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006060 / 0.011353 (-0.005293) | 0.003695 / 0.011008 (-0.007313) | 0.080484 / 0.038508 (0.041976) | 0.061894 / 0.023109 (0.038785) | 0.312510 / 0.275898 (0.036612) | 0.352398 / 0.323480 (0.028918) | 0.004638 / 0.007986 (-0.003348) | 0.002918 / 0.004328 (-0.001410) | 0.062932 / 0.004250 (0.058681) | 0.050859 / 0.037052 (0.013807) | 0.316812 / 0.258489 (0.058323) | 0.357684 / 0.293841 (0.063843) | 0.027622 / 0.128546 (-0.100924) | 0.008012 / 0.075646 (-0.067634) | 0.260970 / 0.419271 (-0.158302) | 0.045807 / 0.043533 (0.002275) | 0.321235 / 0.255139 (0.066096) | 0.343162 / 0.283200 (0.059962) | 0.021136 / 0.141683 (-0.120547) | 1.465886 / 1.452155 (0.013731) | 1.500216 / 1.492716 (0.007500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187286 / 0.018006 (0.169279) | 0.428724 / 0.000490 (0.428235) | 0.003029 / 0.000200 (0.002829) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022703 / 0.037411 (-0.014708) | 0.072740 / 0.014526 (0.058215) | 0.083436 / 0.176557 (-0.093120) | 0.144559 / 0.737135 (-0.592577) | 0.083958 / 0.296338 (-0.212380) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435729 / 0.215209 (0.220520) | 4.351146 / 2.077655 (2.273491) | 2.316627 / 1.504120 (0.812508) | 2.144587 / 1.541195 (0.603393) | 2.209182 / 1.468490 (0.740692) | 0.501131 / 4.584777 (-4.083646) | 3.077085 / 3.745712 (-0.668627) | 4.353706 / 5.269862 (-0.916156) | 2.621523 / 4.565676 (-1.944154) | 0.058976 / 0.424275 (-0.365299) | 0.006467 / 0.007607 (-0.001141) | 0.506690 / 0.226044 (0.280646) | 5.085787 / 2.268929 (2.816858) | 2.731336 / 55.444624 (-52.713289) | 2.419451 / 6.876477 (-4.457025) | 2.583649 / 2.142072 (0.441577) | 0.589869 / 4.805227 (-4.215359) | 0.131040 / 6.500664 (-6.369624) | 0.061332 / 0.075469 (-0.014137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220542 / 1.841788 (-0.621245) | 18.169643 / 8.074308 (10.095335) | 13.251704 / 10.191392 (3.060312) | 0.142952 / 0.680424 (-0.537472) | 0.016639 / 0.534201 (-0.517562) | 0.334851 / 0.579283 (-0.244432) | 0.361865 / 0.434364 (-0.072499) | 0.380933 / 0.540337 (-0.159404) | 0.527374 / 1.386936 (-0.859562) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006319 / 0.011353 (-0.005034) | 0.003778 / 0.011008 (-0.007231) | 0.062388 / 0.038508 (0.023880) | 0.062228 / 0.023109 (0.039119) | 0.373727 / 0.275898 (0.097829) | 0.399442 / 0.323480 (0.075962) | 0.005434 / 0.007986 (-0.002551) | 0.003020 / 0.004328 (-0.001308) | 0.062774 / 0.004250 (0.058524) | 0.052784 / 0.037052 (0.015732) | 0.376428 / 0.258489 (0.117939) | 0.405039 / 0.293841 (0.111198) | 0.027884 / 0.128546 (-0.100662) | 0.008086 / 0.075646 (-0.067561) | 0.067078 / 0.419271 (-0.352194) | 0.042927 / 0.043533 (-0.000606) | 0.372142 / 0.255139 (0.117003) | 0.389604 / 0.283200 (0.106405) | 0.021582 / 0.141683 (-0.120101) | 1.473332 / 1.452155 (0.021177) | 1.536018 / 1.492716 (0.043302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184729 / 0.018006 (0.166723) | 0.421065 / 0.000490 (0.420575) | 0.002681 / 0.000200 (0.002481) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026067 / 0.037411 (-0.011344) | 0.077138 / 0.014526 (0.062612) | 0.085178 / 0.176557 (-0.091379) | 0.139681 / 0.737135 (-0.597454) | 0.087528 / 0.296338 (-0.208810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444899 / 0.215209 (0.229690) | 4.459168 / 2.077655 (2.381513) | 2.408792 / 1.504120 (0.904672) | 2.237243 / 1.541195 (0.696048) | 2.296298 / 1.468490 (0.827808) | 0.498508 / 4.584777 (-4.086269) | 3.067064 / 3.745712 (-0.678648) | 4.470577 / 5.269862 (-0.799284) | 2.701972 / 4.565676 (-1.863705) | 0.057711 / 0.424275 (-0.366564) | 0.006443 / 0.007607 (-0.001164) | 0.524046 / 0.226044 (0.298002) | 5.229928 / 2.268929 (2.961000) | 2.862101 / 55.444624 (-52.582523) | 2.545972 / 6.876477 (-4.330504) | 2.606459 / 2.142072 (0.464387) | 0.593285 / 4.805227 (-4.211942) | 0.124913 / 6.500664 (-6.375751) | 0.061942 / 0.075469 (-0.013527) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322162 / 1.841788 (-0.519625) | 18.745796 / 8.074308 (10.671488) | 13.955443 / 10.191392 (3.764051) | 0.145610 / 0.680424 (-0.534814) | 0.016817 / 0.534201 (-0.517384) | 0.331180 / 0.579283 (-0.248103) | 0.343019 / 0.434364 (-0.091345) | 0.379459 / 0.540337 (-0.160878) | 0.526403 / 1.386936 (-0.860533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aca4cdcc79f16ec5157a2a3a665fdef0e3aa176d \"CML watermark\")\n" ]

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[ "Duplicate of #", "Hi ! can you try to set the upper case environment variables `HTTP_PROXY` and `HTTPS_PROXY` ?\r\n\r\nWe use `aiohttp` for streaming and it uses case sensitive environment variables", "Hi, thanks for the quick reply.\r\n\r\nI set the uppercase env variables with\r\n\r\n`\r\nos.environ['HTTP_PROXY'] = \"http://example.com:xxxx\" \r\nos.environ['HTTPS_PROXY'] = \"http://example.com:xxxx\" \r\n`\r\n\r\nHowever, I still get the same error.\r\n\r\nOne thing that could be helpfull: When downloading a dataset without streaming i get the following message:\r\n_HF google storage unreachable. Downloading and preparing it from source_.\r\nThe download does however work as expected.\r\n", "Are you able to use `aiohttp` to get the file at `https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json` using your proxy ?", "It only works when passing trust_env=True when creating the ClientSession, as well as setting ssl=False.\r\n\r\nWorking Example:\r\n\r\n```\r\nimport os\r\n\r\nos.environ['HTTP_PROXY'] = \"xyz\"\r\nos.environ['HTTPS_PROXY'] = \"xyz\"\r\n\r\nimport asyncio\r\nimport aiohttp\r\n\r\nasync def download_pep(url):\r\n async with aiohttp.ClientSession(trust_env=True) as session:\r\n print(\"1\")\r\n async with session.get(url, ssl=False) as resp:\r\n print(\"2\")\r\n content = await resp.text()\r\n print(content)\r\n return content\r\n\r\nasyncio.run(download_pep(\"https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\"))\r\n```\r\n\r\n\r\n\r\nSSL Verification has been a problem with other packages as well. Usually I circumvent the problem by setting\r\n```\r\nimport ssl\r\nssl._create_default_https_context = ssl._create_unverified_context\r\n```\r\n(probably not the best idea for security), although here aiohttp does not seem to use this default context.", "We do pass `trust_env` as well. Could you share the full stack trace you get when streaming using `datasets` ? That could help locate where we might have forgotten to pass `trust_env`", "Is there a way to disable ssl verification when streaming a dataset. I suspect this might be the isssue with my proxy.\r\n\r\n\r\nHere you go:\r\n\r\n```\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[8], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 ds = load_dataset(\"facebook/voxpopuli\", name=\"de\", streaming=True)\r\n 5 sample = next(iter(ds))\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281), in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1274 dl_manager = StreamingDownloadManager(\r\n 1275 base_path=base_path or self.base_path,\r\n 1276 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1277 dataset_name=self.name,\r\n 1278 data_dir=self.config.data_dir,\r\n 1279 )\r\n 1280 self._check_manual_download(dl_manager)\r\n-> 1281 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1282 # By default, return all splits\r\n 1283 if split is None:\r\n\r\nFile [~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120), in Voxpopuli._split_generators(self, dl_manager)\r\n 118 def _split_generators(self, dl_manager):\r\n 119 n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)\r\n--> 120 with open(n_shards_path) as f:\r\n 121 n_shards = json.load(f)\r\n 123 if self.config.name == \"en_accented\":\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71), in extend_module_for_streaming..wrap_auth..wrapper(*args, **kwargs)\r\n 69 @wraps(function)\r\n 70 def wrapper(*args, **kwargs):\r\n---> 71 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517), in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 515 except FileNotFoundError:\r\n 516 if file.startswith(config.HF_ENDPOINT):\r\n--> 517 raise FileNotFoundError(\r\n 518 file + \"\\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\"\r\n 519 ) from None\r\n 520 else:\r\n 521 raise\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```", "> Is there a way to disable ssl verification when streaming a dataset.\r\n\r\nI don't think so.\r\n\r\nWe use `fsspec` HTTPFileSystem implementation that is based on `aiohttp`. If you register a subclass of HTTPFileSystem that has SSL disabled by default it could work, but I wouldn't recommended it because it can raise security issues.", "Okay thanks for your help! I guess I have to figure out how to improve the proxy environment / see if I can make it work with ssl connections." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006518 / 0.011353 (-0.004835) | 0.004121 / 0.011008 (-0.006887) | 0.103350 / 0.038508 (0.064842) | 0.045030 / 0.023109 (0.021920) | 0.351670 / 0.275898 (0.075772) | 0.408110 / 0.323480 (0.084630) | 0.003883 / 0.007986 (-0.004102) | 0.003352 / 0.004328 (-0.000977) | 0.078786 / 0.004250 (0.074535) | 0.063977 / 0.037052 (0.026925) | 0.369759 / 0.258489 (0.111270) | 0.415103 / 0.293841 (0.121262) | 0.033069 / 0.128546 (-0.095477) | 0.008863 / 0.075646 (-0.066783) | 0.353660 / 0.419271 (-0.065611) | 0.055714 / 0.043533 (0.012181) | 0.350458 / 0.255139 (0.095319) | 0.369505 / 0.283200 (0.086305) | 0.022822 / 0.141683 (-0.118861) | 1.537588 / 1.452155 (0.085433) | 1.590569 / 1.492716 (0.097853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206826 / 0.018006 (0.188819) | 0.471625 / 0.000490 (0.471135) | 0.005188 / 0.000200 (0.004988) | 0.000316 / 0.000054 (0.000261) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028148 / 0.037411 (-0.009263) | 0.111941 / 0.014526 (0.097415) | 0.122106 / 0.176557 (-0.054451) | 0.181127 / 0.737135 (-0.556009) | 0.127534 / 0.296338 (-0.168805) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409520 / 0.215209 (0.194311) | 4.098455 / 2.077655 (2.020800) | 1.852447 / 1.504120 (0.348327) | 1.657036 / 1.541195 (0.115842) | 1.709624 / 1.468490 (0.241134) | 0.542806 / 4.584777 (-4.041970) | 3.809352 / 3.745712 (0.063640) | 1.855412 / 5.269862 (-3.414449) | 1.109180 / 4.565676 (-3.456497) | 0.066801 / 0.424275 (-0.357474) | 0.011832 / 0.007607 (0.004225) | 0.518338 / 0.226044 (0.292293) | 5.190108 / 2.268929 (2.921179) | 2.320602 / 55.444624 (-53.124023) | 1.991416 / 6.876477 (-4.885060) | 2.106989 / 2.142072 (-0.035084) | 0.668914 / 4.805227 (-4.136313) | 0.145325 / 6.500664 (-6.355340) | 0.065145 / 0.075469 (-0.010324) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254706 / 1.841788 (-0.587082) | 14.707264 / 8.074308 (6.632956) | 14.615423 / 10.191392 (4.424031) | 0.170764 / 0.680424 (-0.509659) | 0.017905 / 0.534201 (-0.516296) | 0.435606 / 0.579283 (-0.143677) | 0.434648 / 0.434364 (0.000284) | 0.520813 / 0.540337 (-0.019524) | 0.633902 / 1.386936 (-0.753034) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007212 / 0.011353 (-0.004141) | 0.004301 / 0.011008 (-0.006707) | 0.080767 / 0.038508 (0.042258) | 0.051949 / 0.023109 (0.028840) | 0.398473 / 0.275898 (0.122575) | 0.465038 / 0.323480 (0.141558) | 0.005580 / 0.007986 (-0.002406) | 0.003556 / 0.004328 (-0.000773) | 0.080682 / 0.004250 (0.076431) | 0.059517 / 0.037052 (0.022464) | 0.421171 / 0.258489 (0.162682) | 0.459752 / 0.293841 (0.165911) | 0.032960 / 0.128546 (-0.095586) | 0.009107 / 0.075646 (-0.066539) | 0.086382 / 0.419271 (-0.332889) | 0.056053 / 0.043533 (0.012520) | 0.393357 / 0.255139 (0.138218) | 0.412972 / 0.283200 (0.129772) | 0.031115 / 0.141683 (-0.110568) | 1.576961 / 1.452155 (0.124806) | 1.627249 / 1.492716 (0.134533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227618 / 0.018006 (0.209612) | 0.444640 / 0.000490 (0.444150) | 0.004376 / 0.000200 (0.004176) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006586) | 0.117587 / 0.014526 (0.103062) | 0.127467 / 0.176557 (-0.049089) | 0.184440 / 0.737135 (-0.552695) | 0.133664 / 0.296338 (-0.162675) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443183 / 0.215209 (0.227974) | 4.408312 / 2.077655 (2.330658) | 2.132487 / 1.504120 (0.628367) | 1.923632 / 1.541195 (0.382438) | 1.967882 / 1.468490 (0.499392) | 0.552954 / 4.584777 (-4.031823) | 3.777701 / 3.745712 (0.031989) | 1.857686 / 5.269862 (-3.412176) | 1.104847 / 4.565676 (-3.460829) | 0.068350 / 0.424275 (-0.355925) | 0.012437 / 0.007607 (0.004830) | 0.559258 / 0.226044 (0.333214) | 5.593258 / 2.268929 (3.324330) | 2.648059 / 55.444624 (-52.796565) | 2.277428 / 6.876477 (-4.599049) | 2.351685 / 2.142072 (0.209612) | 0.678750 / 4.805227 (-4.126477) | 0.145550 / 6.500664 (-6.355114) | 0.066556 / 0.075469 (-0.008913) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.327128 / 1.841788 (-0.514659) | 15.649079 / 8.074308 (7.574771) | 14.478659 / 10.191392 (4.287267) | 0.147633 / 0.680424 (-0.532791) | 0.018502 / 0.534201 (-0.515699) | 0.438556 / 0.579283 (-0.140727) | 0.433381 / 0.434364 (-0.000983) | 0.514367 / 0.540337 (-0.025970) | 0.618347 / 1.386936 (-0.768589) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006078 / 0.011353 (-0.005275) | 0.003914 / 0.011008 (-0.007095) | 0.102039 / 0.038508 (0.063531) | 0.037660 / 0.023109 (0.014551) | 0.348963 / 0.275898 (0.073065) | 0.407284 / 0.323480 (0.083804) | 0.004661 / 0.007986 (-0.003324) | 0.003253 / 0.004328 (-0.001076) | 0.078276 / 0.004250 (0.074025) | 0.054144 / 0.037052 (0.017091) | 0.376715 / 0.258489 (0.118225) | 0.418499 / 0.293841 (0.124658) | 0.027627 / 0.128546 (-0.100919) | 0.008494 / 0.075646 (-0.067152) | 0.316894 / 0.419271 (-0.102377) | 0.046560 / 0.043533 (0.003027) | 0.339835 / 0.255139 (0.084696) | 0.374628 / 0.283200 (0.091428) | 0.020729 / 0.141683 (-0.120954) | 1.502769 / 1.452155 (0.050615) | 1.548756 / 1.492716 (0.056040) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229192 / 0.018006 (0.211186) | 0.426245 / 0.000490 (0.425756) | 0.005190 / 0.000200 (0.004990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024271 / 0.037411 (-0.013140) | 0.098869 / 0.014526 (0.084343) | 0.105079 / 0.176557 (-0.071477) | 0.164707 / 0.737135 (-0.572428) | 0.110337 / 0.296338 (-0.186002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426593 / 0.215209 (0.211383) | 4.293977 / 2.077655 (2.216323) | 1.928502 / 1.504120 (0.424382) | 1.728623 / 1.541195 (0.187428) | 1.792084 / 1.468490 (0.323594) | 0.568737 / 4.584777 (-4.016040) | 3.438534 / 3.745712 (-0.307178) | 1.797798 / 5.269862 (-3.472063) | 1.054078 / 4.565676 (-3.511598) | 0.068711 / 0.424275 (-0.355564) | 0.011250 / 0.007607 (0.003643) | 0.529299 / 0.226044 (0.303255) | 5.283965 / 2.268929 (3.015037) | 2.358274 / 55.444624 (-53.086350) | 2.012818 / 6.876477 (-4.863659) | 2.109923 / 2.142072 (-0.032149) | 0.679556 / 4.805227 (-4.125671) | 0.138346 / 6.500664 (-6.362318) | 0.066349 / 0.075469 (-0.009120) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193994 / 1.841788 (-0.647794) | 14.073158 / 8.074308 (5.998850) | 13.488525 / 10.191392 (3.297133) | 0.144536 / 0.680424 (-0.535888) | 0.016748 / 0.534201 (-0.517453) | 0.362703 / 0.579283 (-0.216580) | 0.389511 / 0.434364 (-0.044853) | 0.427296 / 0.540337 (-0.113041) | 0.513227 / 1.386936 (-0.873709) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006215 / 0.011353 (-0.005138) | 0.003834 / 0.011008 (-0.007174) | 0.078001 / 0.038508 (0.039493) | 0.036537 / 0.023109 (0.013428) | 0.369724 / 0.275898 (0.093826) | 0.426761 / 0.323480 (0.103281) | 0.003602 / 0.007986 (-0.004383) | 0.003001 / 0.004328 (-0.001327) | 0.075989 / 0.004250 (0.071739) | 0.048618 / 0.037052 (0.011566) | 0.374296 / 0.258489 (0.115807) | 0.430330 / 0.293841 (0.136489) | 0.028299 / 0.128546 (-0.100247) | 0.008537 / 0.075646 (-0.067109) | 0.083275 / 0.419271 (-0.335997) | 0.043136 / 0.043533 (-0.000397) | 0.359072 / 0.255139 (0.103933) | 0.387391 / 0.283200 (0.104192) | 0.021202 / 0.141683 (-0.120481) | 1.520832 / 1.452155 (0.068677) | 1.567030 / 1.492716 (0.074313) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230944 / 0.018006 (0.212938) | 0.422159 / 0.000490 (0.421669) | 0.003447 / 0.000200 (0.003247) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025442 / 0.037411 (-0.011969) | 0.103944 / 0.014526 (0.089418) | 0.110577 / 0.176557 (-0.065979) | 0.161393 / 0.737135 (-0.575743) | 0.113482 / 0.296338 (-0.182857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485765 / 0.215209 (0.270556) | 4.845737 / 2.077655 (2.768083) | 2.556732 / 1.504120 (1.052612) | 2.348638 / 1.541195 (0.807443) | 2.379289 / 1.468490 (0.910799) | 0.561261 / 4.584777 (-4.023516) | 3.482468 / 3.745712 (-0.263244) | 3.061319 / 5.269862 (-2.208543) | 1.483938 / 4.565676 (-3.081738) | 0.067584 / 0.424275 (-0.356691) | 0.011333 / 0.007607 (0.003726) | 0.594342 / 0.226044 (0.368297) | 5.935477 / 2.268929 (3.666548) | 3.025029 / 55.444624 (-52.419595) | 2.687032 / 6.876477 (-4.189445) | 2.752470 / 2.142072 (0.610398) | 0.674470 / 4.805227 (-4.130757) | 0.136777 / 6.500664 (-6.363887) | 0.068335 / 0.075469 (-0.007134) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336456 / 1.841788 (-0.505332) | 14.376007 / 8.074308 (6.301699) | 14.171375 / 10.191392 (3.979983) | 0.159620 / 0.680424 (-0.520804) | 0.016685 / 0.534201 (-0.517516) | 0.364344 / 0.579283 (-0.214939) | 0.395358 / 0.434364 (-0.039006) | 0.424876 / 0.540337 (-0.115461) | 0.513267 / 1.386936 (-0.873669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006983 / 0.011353 (-0.004369) | 0.004473 / 0.011008 (-0.006535) | 0.105158 / 0.038508 (0.066650) | 0.048973 / 0.023109 (0.025864) | 0.358771 / 0.275898 (0.082873) | 0.432389 / 0.323480 (0.108909) | 0.005689 / 0.007986 (-0.002297) | 0.003584 / 0.004328 (-0.000744) | 0.080852 / 0.004250 (0.076601) | 0.066133 / 0.037052 (0.029081) | 0.370981 / 0.258489 (0.112492) | 0.406942 / 0.293841 (0.113101) | 0.032123 / 0.128546 (-0.096424) | 0.009313 / 0.075646 (-0.066333) | 0.355220 / 0.419271 (-0.064051) | 0.055768 / 0.043533 (0.012235) | 0.370545 / 0.255139 (0.115406) | 0.375619 / 0.283200 (0.092419) | 0.024258 / 0.141683 (-0.117425) | 1.559073 / 1.452155 (0.106918) | 1.616520 / 1.492716 (0.123804) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277893 / 0.018006 (0.259887) | 0.535447 / 0.000490 (0.534957) | 0.004877 / 0.000200 (0.004677) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029444 / 0.037411 (-0.007968) | 0.114366 / 0.014526 (0.099841) | 0.130957 / 0.176557 (-0.045599) | 0.189604 / 0.737135 (-0.547531) | 0.131682 / 0.296338 (-0.164656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412315 / 0.215209 (0.197106) | 4.093879 / 2.077655 (2.016225) | 1.856169 / 1.504120 (0.352050) | 1.655358 / 1.541195 (0.114164) | 1.758190 / 1.468490 (0.289699) | 0.545829 / 4.584777 (-4.038948) | 3.871436 / 3.745712 (0.125724) | 1.938244 / 5.269862 (-3.331618) | 1.122727 / 4.565676 (-3.442950) | 0.067107 / 0.424275 (-0.357168) | 0.012012 / 0.007607 (0.004405) | 0.518868 / 0.226044 (0.292824) | 5.235081 / 2.268929 (2.966153) | 2.335115 / 55.444624 (-53.109509) | 2.013074 / 6.876477 (-4.863402) | 2.219808 / 2.142072 (0.077735) | 0.674602 / 4.805227 (-4.130626) | 0.147051 / 6.500664 (-6.353613) | 0.068444 / 0.075469 (-0.007025) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245600 / 1.841788 (-0.596188) | 15.537727 / 8.074308 (7.463419) | 15.074300 / 10.191392 (4.882908) | 0.194217 / 0.680424 (-0.486207) | 0.018536 / 0.534201 (-0.515665) | 0.437085 / 0.579283 (-0.142198) | 0.441123 / 0.434364 (0.006759) | 0.530681 / 0.540337 (-0.009657) | 0.649154 / 1.386936 (-0.737782) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007243 / 0.011353 (-0.004110) | 0.004688 / 0.011008 (-0.006320) | 0.079809 / 0.038508 (0.041301) | 0.046915 / 0.023109 (0.023805) | 0.415144 / 0.275898 (0.139246) | 0.474867 / 0.323480 (0.151388) | 0.004550 / 0.007986 (-0.003435) | 0.004585 / 0.004328 (0.000257) | 0.080837 / 0.004250 (0.076587) | 0.061667 / 0.037052 (0.024614) | 0.411321 / 0.258489 (0.152832) | 0.464195 / 0.293841 (0.170354) | 0.032510 / 0.128546 (-0.096037) | 0.009306 / 0.075646 (-0.066340) | 0.086637 / 0.419271 (-0.332635) | 0.053335 / 0.043533 (0.009802) | 0.402302 / 0.255139 (0.147163) | 0.424864 / 0.283200 (0.141664) | 0.026573 / 0.141683 (-0.115110) | 1.566793 / 1.452155 (0.114639) | 1.628118 / 1.492716 (0.135401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317802 / 0.018006 (0.299796) | 0.544593 / 0.000490 (0.544103) | 0.005690 / 0.000200 (0.005490) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033015 / 0.037411 (-0.004397) | 0.121940 / 0.014526 (0.107414) | 0.132920 / 0.176557 (-0.043637) | 0.191481 / 0.737135 (-0.545655) | 0.139139 / 0.296338 (-0.157199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460382 / 0.215209 (0.245173) | 4.610046 / 2.077655 (2.532392) | 2.296573 / 1.504120 (0.792453) | 2.099735 / 1.541195 (0.558540) | 2.213913 / 1.468490 (0.745423) | 0.544871 / 4.584777 (-4.039906) | 3.814174 / 3.745712 (0.068462) | 3.246397 / 5.269862 (-2.023464) | 1.480236 / 4.565676 (-3.085440) | 0.068464 / 0.424275 (-0.355811) | 0.012651 / 0.007607 (0.005043) | 0.564989 / 0.226044 (0.338944) | 5.639188 / 2.268929 (3.370259) | 2.827601 / 55.444624 (-52.617023) | 2.473743 / 6.876477 (-4.402734) | 2.567413 / 2.142072 (0.425340) | 0.674351 / 4.805227 (-4.130876) | 0.146248 / 6.500664 (-6.354416) | 0.067553 / 0.075469 (-0.007916) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346703 / 1.841788 (-0.495085) | 16.494787 / 8.074308 (8.420479) | 15.179487 / 10.191392 (4.988095) | 0.181864 / 0.680424 (-0.498560) | 0.018857 / 0.534201 (-0.515344) | 0.437787 / 0.579283 (-0.141496) | 0.431770 / 0.434364 (-0.002594) | 0.507116 / 0.540337 (-0.033221) | 0.608899 / 1.386936 (-0.778037) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fd5b7412f907675e76b183a6e39ef6d176fdcc0 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005963 / 0.011353 (-0.005390) | 0.003743 / 0.011008 (-0.007265) | 0.098519 / 0.038508 (0.060011) | 0.037392 / 0.023109 (0.014283) | 0.322706 / 0.275898 (0.046808) | 0.380032 / 0.323480 (0.056552) | 0.004694 / 0.007986 (-0.003292) | 0.002897 / 0.004328 (-0.001432) | 0.078664 / 0.004250 (0.074414) | 0.052646 / 0.037052 (0.015594) | 0.335523 / 0.258489 (0.077034) | 0.375464 / 0.293841 (0.081623) | 0.027537 / 0.128546 (-0.101010) | 0.008452 / 0.075646 (-0.067194) | 0.313844 / 0.419271 (-0.105427) | 0.047368 / 0.043533 (0.003835) | 0.313833 / 0.255139 (0.058694) | 0.342284 / 0.283200 (0.059085) | 0.021136 / 0.141683 (-0.120547) | 1.544764 / 1.452155 (0.092610) | 1.563850 / 1.492716 (0.071134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188609 / 0.018006 (0.170603) | 0.421686 / 0.000490 (0.421196) | 0.003336 / 0.000200 (0.003136) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023678 / 0.037411 (-0.013733) | 0.099191 / 0.014526 (0.084665) | 0.105819 / 0.176557 (-0.070738) | 0.169654 / 0.737135 (-0.567481) | 0.110240 / 0.296338 (-0.186099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425497 / 0.215209 (0.210288) | 4.237165 / 2.077655 (2.159510) | 1.902953 / 1.504120 (0.398833) | 1.699012 / 1.541195 (0.157818) | 1.751107 / 1.468490 (0.282617) | 0.563326 / 4.584777 (-4.021451) | 3.394189 / 3.745712 (-0.351523) | 2.706129 / 5.269862 (-2.563732) | 1.361522 / 4.565676 (-3.204155) | 0.067776 / 0.424275 (-0.356499) | 0.010959 / 0.007607 (0.003352) | 0.530905 / 0.226044 (0.304860) | 5.322467 / 2.268929 (3.053538) | 2.384356 / 55.444624 (-53.060269) | 2.044196 / 6.876477 (-4.832281) | 2.119837 / 2.142072 (-0.022235) | 0.682236 / 4.805227 (-4.122991) | 0.136921 / 6.500664 (-6.363743) | 0.066784 / 0.075469 (-0.008685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210642 / 1.841788 (-0.631146) | 13.804572 / 8.074308 (5.730264) | 13.309229 / 10.191392 (3.117837) | 0.154356 / 0.680424 (-0.526068) | 0.016833 / 0.534201 (-0.517368) | 0.366503 / 0.579283 (-0.212780) | 0.385201 / 0.434364 (-0.049163) | 0.426713 / 0.540337 (-0.113624) | 0.516795 / 1.386936 (-0.870141) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006144 / 0.011353 (-0.005209) | 0.003723 / 0.011008 (-0.007285) | 0.077427 / 0.038508 (0.038919) | 0.037636 / 0.023109 (0.014527) | 0.375048 / 0.275898 (0.099150) | 0.442254 / 0.323480 (0.118774) | 0.003506 / 0.007986 (-0.004480) | 0.003751 / 0.004328 (-0.000577) | 0.076771 / 0.004250 (0.072521) | 0.047915 / 0.037052 (0.010862) | 0.378918 / 0.258489 (0.120429) | 0.435300 / 0.293841 (0.141459) | 0.028317 / 0.128546 (-0.100230) | 0.008413 / 0.075646 (-0.067233) | 0.082774 / 0.419271 (-0.336497) | 0.043211 / 0.043533 (-0.000321) | 0.362022 / 0.255139 (0.106883) | 0.404928 / 0.283200 (0.121728) | 0.020692 / 0.141683 (-0.120991) | 1.527303 / 1.452155 (0.075148) | 1.596091 / 1.492716 (0.103375) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225537 / 0.018006 (0.207530) | 0.399901 / 0.000490 (0.399412) | 0.000424 / 0.000200 (0.000224) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026483 / 0.037411 (-0.010928) | 0.104373 / 0.014526 (0.089847) | 0.111271 / 0.176557 (-0.065286) | 0.163872 / 0.737135 (-0.573264) | 0.113991 / 0.296338 (-0.182347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456484 / 0.215209 (0.241275) | 4.572652 / 2.077655 (2.494998) | 2.374908 / 1.504120 (0.870788) | 2.207855 / 1.541195 (0.666661) | 2.260009 / 1.468490 (0.791519) | 0.562678 / 4.584777 (-4.022099) | 3.441778 / 3.745712 (-0.303934) | 1.729006 / 5.269862 (-3.540855) | 1.024937 / 4.565676 (-3.540739) | 0.068707 / 0.424275 (-0.355568) | 0.011334 / 0.007607 (0.003727) | 0.564293 / 0.226044 (0.338248) | 5.638367 / 2.268929 (3.369438) | 2.665654 / 55.444624 (-52.778970) | 2.320033 / 6.876477 (-4.556444) | 2.328706 / 2.142072 (0.186634) | 0.677433 / 4.805227 (-4.127794) | 0.137190 / 6.500664 (-6.363474) | 0.068585 / 0.075469 (-0.006885) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312476 / 1.841788 (-0.529312) | 14.206685 / 8.074308 (6.132377) | 14.217928 / 10.191392 (4.026536) | 0.143416 / 0.680424 (-0.537007) | 0.016647 / 0.534201 (-0.517554) | 0.361228 / 0.579283 (-0.218055) | 0.396185 / 0.434364 (-0.038178) | 0.423275 / 0.540337 (-0.117063) | 0.512966 / 1.386936 (-0.873970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b424648fd68bd0b5279eb916cec4836d1220e268 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008913 / 0.011353 (-0.002440) | 0.005142 / 0.011008 (-0.005866) | 0.133958 / 0.038508 (0.095449) | 0.049180 / 0.023109 (0.026071) | 0.389169 / 0.275898 (0.113270) | 0.481513 / 0.323480 (0.158033) | 0.006555 / 0.007986 (-0.001430) | 0.003806 / 0.004328 (-0.000522) | 0.102056 / 0.004250 (0.097806) | 0.083259 / 0.037052 (0.046207) | 0.392536 / 0.258489 (0.134047) | 0.447503 / 0.293841 (0.153662) | 0.047472 / 0.128546 (-0.081074) | 0.014748 / 0.075646 (-0.060899) | 0.475619 / 0.419271 (0.056348) | 0.107306 / 0.043533 (0.063773) | 0.421942 / 0.255139 (0.166803) | 0.419736 / 0.283200 (0.136536) | 0.044195 / 0.141683 (-0.097488) | 1.793840 / 1.452155 (0.341686) | 1.960204 / 1.492716 (0.467488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252046 / 0.018006 (0.234040) | 0.627725 / 0.000490 (0.627236) | 0.007435 / 0.000200 (0.007235) | 0.000526 / 0.000054 (0.000472) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034656 / 0.037411 (-0.002755) | 0.114534 / 0.014526 (0.100008) | 0.135804 / 0.176557 (-0.040753) | 0.209309 / 0.737135 (-0.527826) | 0.140369 / 0.296338 (-0.155969) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636736 / 0.215209 (0.421527) | 6.039985 / 2.077655 (3.962330) | 2.640141 / 1.504120 (1.136021) | 2.284492 / 1.541195 (0.743297) | 2.324956 / 1.468490 (0.856466) | 0.934499 / 4.584777 (-3.650278) | 5.673415 / 3.745712 (1.927703) | 5.184584 / 5.269862 (-0.085278) | 2.661911 / 4.565676 (-1.903766) | 0.150420 / 0.424275 (-0.273855) | 0.015655 / 0.007607 (0.008048) | 0.748290 / 0.226044 (0.522246) | 7.579755 / 2.268929 (5.310827) | 3.346732 / 55.444624 (-52.097892) | 2.708212 / 6.876477 (-4.168264) | 2.682423 / 2.142072 (0.540351) | 1.170389 / 4.805227 (-3.634838) | 0.215775 / 6.500664 (-6.284889) | 0.076360 / 0.075469 (0.000891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.516794 / 1.841788 (-0.324993) | 18.709117 / 8.074308 (10.634809) | 22.492542 / 10.191392 (12.301150) | 0.237978 / 0.680424 (-0.442446) | 0.027828 / 0.534201 (-0.506373) | 0.499968 / 0.579283 (-0.079315) | 0.645899 / 0.434364 (0.211535) | 0.548599 / 0.540337 (0.008262) | 0.675428 / 1.386936 (-0.711508) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008469 / 0.011353 (-0.002884) | 0.005420 / 0.011008 (-0.005589) | 0.093340 / 0.038508 (0.054832) | 0.045896 / 0.023109 (0.022786) | 0.533267 / 0.275898 (0.257369) | 0.596034 / 0.323480 (0.272555) | 0.004816 / 0.007986 (-0.003170) | 0.004379 / 0.004328 (0.000051) | 0.096356 / 0.004250 (0.092106) | 0.058339 / 0.037052 (0.021287) | 0.574464 / 0.258489 (0.315975) | 0.649301 / 0.293841 (0.355461) | 0.047599 / 0.128546 (-0.080947) | 0.013759 / 0.075646 (-0.061887) | 0.104672 / 0.419271 (-0.314599) | 0.061658 / 0.043533 (0.018125) | 0.560956 / 0.255139 (0.305817) | 0.585328 / 0.283200 (0.302128) | 0.034137 / 0.141683 (-0.107546) | 1.844528 / 1.452155 (0.392373) | 1.971398 / 1.492716 (0.478682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278666 / 0.018006 (0.260660) | 0.577342 / 0.000490 (0.576853) | 0.005496 / 0.000200 (0.005296) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029863 / 0.037411 (-0.007549) | 0.161703 / 0.014526 (0.147177) | 0.132279 / 0.176557 (-0.044277) | 0.227345 / 0.737135 (-0.509791) | 0.138047 / 0.296338 (-0.158291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.651535 / 0.215209 (0.436326) | 7.077949 / 2.077655 (5.000295) | 2.926990 / 1.504120 (1.422871) | 2.598872 / 1.541195 (1.057678) | 2.614192 / 1.468490 (1.145702) | 0.913845 / 4.584777 (-3.670932) | 5.704301 / 3.745712 (1.958589) | 2.796914 / 5.269862 (-2.472948) | 1.836096 / 4.565676 (-2.729580) | 0.106294 / 0.424275 (-0.317981) | 0.012705 / 0.007607 (0.005098) | 0.836336 / 0.226044 (0.610291) | 8.234079 / 2.268929 (5.965150) | 3.836410 / 55.444624 (-51.608215) | 3.116752 / 6.876477 (-3.759724) | 3.154258 / 2.142072 (1.012186) | 1.195794 / 4.805227 (-3.609434) | 0.240491 / 6.500664 (-6.260173) | 0.087913 / 0.075469 (0.012444) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.724723 / 1.841788 (-0.117064) | 19.492194 / 8.074308 (11.417885) | 21.443341 / 10.191392 (11.251949) | 0.245819 / 0.680424 (-0.434605) | 0.027024 / 0.534201 (-0.507177) | 0.481071 / 0.579283 (-0.098212) | 0.596359 / 0.434364 (0.161995) | 0.646462 / 0.540337 (0.106124) | 0.706380 / 1.386936 (-0.680556) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ca664e6d5ef137127b238aae1d0aff54e22db2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006634 / 0.011353 (-0.004719) | 0.004003 / 0.011008 (-0.007005) | 0.097874 / 0.038508 (0.059365) | 0.043528 / 0.023109 (0.020419) | 0.302293 / 0.275898 (0.026395) | 0.357041 / 0.323480 (0.033561) | 0.003761 / 0.007986 (-0.004225) | 0.004312 / 0.004328 (-0.000016) | 0.076253 / 0.004250 (0.072003) | 0.062807 / 0.037052 (0.025755) | 0.316737 / 0.258489 (0.058248) | 0.356722 / 0.293841 (0.062881) | 0.030816 / 0.128546 (-0.097730) | 0.008691 / 0.075646 (-0.066955) | 0.328366 / 0.419271 (-0.090906) | 0.062299 / 0.043533 (0.018766) | 0.293877 / 0.255139 (0.038738) | 0.319832 / 0.283200 (0.036632) | 0.024996 / 0.141683 (-0.116687) | 1.473912 / 1.452155 (0.021758) | 1.565439 / 1.492716 (0.072723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208428 / 0.018006 (0.190422) | 0.435618 / 0.000490 (0.435128) | 0.000695 / 0.000200 (0.000495) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011158) | 0.106908 / 0.014526 (0.092382) | 0.117075 / 0.176557 (-0.059482) | 0.177969 / 0.737135 (-0.559166) | 0.123400 / 0.296338 (-0.172938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424970 / 0.215209 (0.209761) | 4.203233 / 2.077655 (2.125578) | 2.009679 / 1.504120 (0.505559) | 1.825691 / 1.541195 (0.284496) | 1.870639 / 1.468490 (0.402149) | 0.530758 / 4.584777 (-4.054019) | 3.718791 / 3.745712 (-0.026921) | 1.800206 / 5.269862 (-3.469656) | 1.071651 / 4.565676 (-3.494025) | 0.065126 / 0.424275 (-0.359149) | 0.011312 / 0.007607 (0.003704) | 0.532503 / 0.226044 (0.306458) | 5.353950 / 2.268929 (3.085021) | 2.463548 / 55.444624 (-52.981076) | 2.139832 / 6.876477 (-4.736645) | 2.238722 / 2.142072 (0.096650) | 0.655736 / 4.805227 (-4.149492) | 0.141689 / 6.500664 (-6.358975) | 0.063282 / 0.075469 (-0.012187) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183523 / 1.841788 (-0.658265) | 14.146428 / 8.074308 (6.072120) | 14.312883 / 10.191392 (4.121491) | 0.169286 / 0.680424 (-0.511138) | 0.017343 / 0.534201 (-0.516858) | 0.397934 / 0.579283 (-0.181349) | 0.417791 / 0.434364 (-0.016573) | 0.463639 / 0.540337 (-0.076698) | 0.562787 / 1.386936 (-0.824149) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006594 / 0.011353 (-0.004759) | 0.004086 / 0.011008 (-0.006922) | 0.075122 / 0.038508 (0.036614) | 0.041849 / 0.023109 (0.018740) | 0.362645 / 0.275898 (0.086747) | 0.464350 / 0.323480 (0.140870) | 0.003760 / 0.007986 (-0.004226) | 0.003327 / 0.004328 (-0.001001) | 0.076154 / 0.004250 (0.071904) | 0.053232 / 0.037052 (0.016180) | 0.407863 / 0.258489 (0.149374) | 0.460787 / 0.293841 (0.166946) | 0.031917 / 0.128546 (-0.096630) | 0.008770 / 0.075646 (-0.066876) | 0.082612 / 0.419271 (-0.336660) | 0.051311 / 0.043533 (0.007779) | 0.354508 / 0.255139 (0.099369) | 0.419533 / 0.283200 (0.136334) | 0.023980 / 0.141683 (-0.117703) | 1.491255 / 1.452155 (0.039100) | 1.536101 / 1.492716 (0.043384) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178261 / 0.018006 (0.160255) | 0.444680 / 0.000490 (0.444190) | 0.013761 / 0.000200 (0.013561) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027875 / 0.037411 (-0.009536) | 0.111269 / 0.014526 (0.096744) | 0.121096 / 0.176557 (-0.055461) | 0.174387 / 0.737135 (-0.562749) | 0.124714 / 0.296338 (-0.171624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445422 / 0.215209 (0.230213) | 4.435877 / 2.077655 (2.358222) | 2.221895 / 1.504120 (0.717775) | 2.030571 / 1.541195 (0.489376) | 2.074863 / 1.468490 (0.606373) | 0.543331 / 4.584777 (-4.041446) | 3.753615 / 3.745712 (0.007903) | 3.317074 / 5.269862 (-1.952787) | 1.630390 / 4.565676 (-2.935286) | 0.066726 / 0.424275 (-0.357549) | 0.011556 / 0.007607 (0.003949) | 0.546985 / 0.226044 (0.320941) | 5.460634 / 2.268929 (3.191705) | 2.705945 / 55.444624 (-52.738679) | 2.373425 / 6.876477 (-4.503052) | 2.401472 / 2.142072 (0.259399) | 0.663225 / 4.805227 (-4.142002) | 0.143694 / 6.500664 (-6.356970) | 0.065283 / 0.075469 (-0.010186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264804 / 1.841788 (-0.576983) | 14.803228 / 8.074308 (6.728919) | 14.178514 / 10.191392 (3.987122) | 0.162651 / 0.680424 (-0.517772) | 0.017586 / 0.534201 (-0.516615) | 0.398740 / 0.579283 (-0.180543) | 0.414478 / 0.434364 (-0.019886) | 0.465442 / 0.540337 (-0.074895) | 0.563450 / 1.386936 (-0.823486) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76f75a9a3b2aaad05ea0ea5ab77e01fd2ca66760 \"CML watermark\")\n" ]

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[ "Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ", "Is this issue open? If so, I will self assign. ", "@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.", "I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you *how* to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.", "#self-assign" ]

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[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n", "Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]

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[ "cc @pcuenca as well. \r\n\r\nNot super urgent btw", "The issue commes from the dataset itself and is not related to the `datasets` lib\r\n\r\nsee https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/blob/2c475b3b88e0f2e5828f830a4b91618a25ff20b7/common_voice_6_1.py#L148-L152", "Let's remove these lines in the dataset no? cc @anton-l @Vaibhavs10 ", "Addressed in:\r\n\r\n* `mozilla-foundation/common_voice_1_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_1_0/discussions/4)\r\n* `mozilla-foundation/common_voice_2_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_2_0/discussions/3)\r\n* `mozilla-foundation/common_voice_3_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0/discussions/3)\r\n* `mozilla-foundation/common_voice_4_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_4_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_1/discussions/3)\r\n* `mozilla-foundation/common_voice_6_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_0/discussions/3)\r\n* `mozilla-foundation/common_voice_6_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/discussions/3)\r\n* `mozilla-foundation/common_voice_7_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/discussions/3)\r\n* `mozilla-foundation/common_voice_8_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/7)\r\n* `mozilla-foundation/common_voice_9_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0/discussions/8)\r\n* `mozilla-foundation/common_voice_10_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_10_0/discussions/7)" ]

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[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split", "That sounds like a clean workaround!" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006186 / 0.011353 (-0.005167) | 0.003744 / 0.011008 (-0.007264) | 0.097295 / 0.038508 (0.058787) | 0.037106 / 0.023109 (0.013997) | 0.424154 / 0.275898 (0.148256) | 0.474536 / 0.323480 (0.151057) | 0.003454 / 0.007986 (-0.004532) | 0.003865 / 0.004328 (-0.000463) | 0.077348 / 0.004250 (0.073097) | 0.051728 / 0.037052 (0.014675) | 0.437120 / 0.258489 (0.178631) | 0.478379 / 0.293841 (0.184538) | 0.028939 / 0.128546 (-0.099608) | 0.008376 / 0.075646 (-0.067270) | 0.312002 / 0.419271 (-0.107270) | 0.053723 / 0.043533 (0.010190) | 0.424815 / 0.255139 (0.169676) | 0.446203 / 0.283200 (0.163004) | 0.026553 / 0.141683 (-0.115130) | 1.479983 / 1.452155 (0.027828) | 1.530613 / 1.492716 (0.037896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196627 / 0.018006 (0.178620) | 0.422361 / 0.000490 (0.421871) | 0.003442 / 0.000200 (0.003242) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.096011 / 0.014526 (0.081485) | 0.104091 / 0.176557 (-0.072466) | 0.163273 / 0.737135 (-0.573862) | 0.109142 / 0.296338 (-0.187197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431032 / 0.215209 (0.215823) | 4.314391 / 2.077655 (2.236737) | 2.003812 / 1.504120 (0.499692) | 1.799538 / 1.541195 (0.258344) | 1.830026 / 1.468490 (0.361536) | 0.560131 / 4.584777 (-4.024646) | 3.368997 / 3.745712 (-0.376715) | 1.703032 / 5.269862 (-3.566830) | 1.026949 / 4.565676 (-3.538727) | 0.067507 / 0.424275 (-0.356768) | 0.010910 / 0.007607 (0.003303) | 0.532606 / 0.226044 (0.306562) | 5.345179 / 2.268929 (3.076250) | 2.368077 / 55.444624 (-53.076548) | 2.028913 / 6.876477 (-4.847564) | 2.147621 / 2.142072 (0.005549) | 0.675696 / 4.805227 (-4.129531) | 0.134902 / 6.500664 (-6.365762) | 0.065004 / 0.075469 (-0.010465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233412 / 1.841788 (-0.608376) | 13.767465 / 8.074308 (5.693157) | 13.933653 / 10.191392 (3.742261) | 0.129010 / 0.680424 (-0.551414) | 0.016708 / 0.534201 (-0.517493) | 0.362341 / 0.579283 (-0.216942) | 0.390902 / 0.434364 (-0.043462) | 0.429156 / 0.540337 (-0.111182) | 0.521166 / 1.386936 (-0.865770) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006169 / 0.011353 (-0.005184) | 0.003839 / 0.011008 (-0.007169) | 0.078784 / 0.038508 (0.040276) | 0.040218 / 0.023109 (0.017109) | 0.360439 / 0.275898 (0.084541) | 0.423957 / 0.323480 (0.100477) | 0.003456 / 0.007986 (-0.004529) | 0.002900 / 0.004328 (-0.001428) | 0.078820 / 0.004250 (0.074569) | 0.047240 / 0.037052 (0.010187) | 0.372081 / 0.258489 (0.113592) | 0.424263 / 0.293841 (0.130422) | 0.027977 / 0.128546 (-0.100569) | 0.008400 / 0.075646 (-0.067246) | 0.084399 / 0.419271 (-0.334872) | 0.043303 / 0.043533 (-0.000230) | 0.361583 / 0.255139 (0.106444) | 0.394987 / 0.283200 (0.111787) | 0.020006 / 0.141683 (-0.121677) | 1.520208 / 1.452155 (0.068053) | 1.587335 / 1.492716 (0.094619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223847 / 0.018006 (0.205840) | 0.402194 / 0.000490 (0.401704) | 0.000384 / 0.000200 (0.000184) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024902 / 0.037411 (-0.012509) | 0.099076 / 0.014526 (0.084550) | 0.108041 / 0.176557 (-0.068516) | 0.159385 / 0.737135 (-0.577750) | 0.111442 / 0.296338 (-0.184896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446232 / 0.215209 (0.231023) | 4.464927 / 2.077655 (2.387272) | 2.155234 / 1.504120 (0.651114) | 1.953645 / 1.541195 (0.412450) | 1.965991 / 1.468490 (0.497501) | 0.553473 / 4.584777 (-4.031304) | 3.321397 / 3.745712 (-0.424315) | 1.693761 / 5.269862 (-3.576101) | 1.006299 / 4.565676 (-3.559378) | 0.067013 / 0.424275 (-0.357262) | 0.011116 / 0.007607 (0.003509) | 0.555014 / 0.226044 (0.328970) | 5.535694 / 2.268929 (3.266765) | 2.598339 / 55.444624 (-52.846285) | 2.249298 / 6.876477 (-4.627179) | 2.243419 / 2.142072 (0.101347) | 0.667603 / 4.805227 (-4.137624) | 0.133322 / 6.500664 (-6.367343) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293051 / 1.841788 (-0.548737) | 14.103731 / 8.074308 (6.029423) | 14.215204 / 10.191392 (4.023812) | 0.143990 / 0.680424 (-0.536434) | 0.016805 / 0.534201 (-0.517396) | 0.363264 / 0.579283 (-0.216019) | 0.392769 / 0.434364 (-0.041594) | 0.425291 / 0.540337 (-0.115046) | 0.515479 / 1.386936 (-0.871457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006346 / 0.011353 (-0.005006) | 0.004130 / 0.011008 (-0.006878) | 0.096898 / 0.038508 (0.058390) | 0.042564 / 0.023109 (0.019455) | 0.343748 / 0.275898 (0.067850) | 0.412515 / 0.323480 (0.089035) | 0.006153 / 0.007986 (-0.001833) | 0.003345 / 0.004328 (-0.000984) | 0.075314 / 0.004250 (0.071064) | 0.061478 / 0.037052 (0.024426) | 0.362948 / 0.258489 (0.104459) | 0.401533 / 0.293841 (0.107692) | 0.032363 / 0.128546 (-0.096184) | 0.008780 / 0.075646 (-0.066867) | 0.328691 / 0.419271 (-0.090580) | 0.054253 / 0.043533 (0.010721) | 0.340783 / 0.255139 (0.085644) | 0.360705 / 0.283200 (0.077505) | 0.023183 / 0.141683 (-0.118500) | 1.484078 / 1.452155 (0.031924) | 1.528581 / 1.492716 (0.035865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208732 / 0.018006 (0.190726) | 0.452572 / 0.000490 (0.452082) | 0.002936 / 0.000200 (0.002737) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024616 / 0.037411 (-0.012795) | 0.107547 / 0.014526 (0.093021) | 0.114492 / 0.176557 (-0.062065) | 0.171770 / 0.737135 (-0.565365) | 0.122538 / 0.296338 (-0.173800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406140 / 0.215209 (0.190930) | 4.062391 / 2.077655 (1.984736) | 1.865962 / 1.504120 (0.361842) | 1.682236 / 1.541195 (0.141041) | 1.738119 / 1.468490 (0.269629) | 0.532244 / 4.584777 (-4.052533) | 3.816421 / 3.745712 (0.070709) | 2.981205 / 5.269862 (-2.288656) | 1.519497 / 4.565676 (-3.046179) | 0.065904 / 0.424275 (-0.358371) | 0.011277 / 0.007607 (0.003670) | 0.512789 / 0.226044 (0.286745) | 5.107618 / 2.268929 (2.838690) | 2.419399 / 55.444624 (-53.025226) | 2.079262 / 6.876477 (-4.797214) | 2.150447 / 2.142072 (0.008375) | 0.696737 / 4.805227 (-4.108490) | 0.142497 / 6.500664 (-6.358167) | 0.063521 / 0.075469 (-0.011949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180692 / 1.841788 (-0.661095) | 14.343084 / 8.074308 (6.268776) | 13.303719 / 10.191392 (3.112327) | 0.164234 / 0.680424 (-0.516190) | 0.017439 / 0.534201 (-0.516762) | 0.399712 / 0.579283 (-0.179571) | 0.428248 / 0.434364 (-0.006115) | 0.471909 / 0.540337 (-0.068428) | 0.573853 / 1.386936 (-0.813083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006210 / 0.011353 (-0.005143) | 0.004104 / 0.011008 (-0.006905) | 0.075140 / 0.038508 (0.036632) | 0.044647 / 0.023109 (0.021538) | 0.370120 / 0.275898 (0.094222) | 0.452936 / 0.323480 (0.129457) | 0.003943 / 0.007986 (-0.004042) | 0.003285 / 0.004328 (-0.001043) | 0.075267 / 0.004250 (0.071017) | 0.055517 / 0.037052 (0.018465) | 0.396385 / 0.258489 (0.137896) | 0.447870 / 0.293841 (0.154029) | 0.031342 / 0.128546 (-0.097204) | 0.008720 / 0.075646 (-0.066926) | 0.082702 / 0.419271 (-0.336570) | 0.051010 / 0.043533 (0.007477) | 0.350546 / 0.255139 (0.095407) | 0.425395 / 0.283200 (0.142195) | 0.024483 / 0.141683 (-0.117200) | 1.467341 / 1.452155 (0.015186) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218067 / 0.018006 (0.200061) | 0.441603 / 0.000490 (0.441114) | 0.003711 / 0.000200 (0.003512) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028669 / 0.037411 (-0.008742) | 0.112941 / 0.014526 (0.098415) | 0.122584 / 0.176557 (-0.053972) | 0.176494 / 0.737135 (-0.560641) | 0.129369 / 0.296338 (-0.166970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434543 / 0.215209 (0.219334) | 4.344056 / 2.077655 (2.266401) | 2.079286 / 1.504120 (0.575166) | 1.887264 / 1.541195 (0.346069) | 1.910386 / 1.468490 (0.441896) | 0.538824 / 4.584777 (-4.045953) | 3.844786 / 3.745712 (0.099074) | 2.902091 / 5.269862 (-2.367770) | 1.270852 / 4.565676 (-3.294824) | 0.066324 / 0.424275 (-0.357951) | 0.011346 / 0.007607 (0.003739) | 0.537122 / 0.226044 (0.311078) | 5.367354 / 2.268929 (3.098426) | 2.533672 / 55.444624 (-52.910952) | 2.203260 / 6.876477 (-4.673217) | 2.224310 / 2.142072 (0.082237) | 0.663806 / 4.805227 (-4.141422) | 0.142758 / 6.500664 (-6.357906) | 0.063870 / 0.075469 (-0.011599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260487 / 1.841788 (-0.581301) | 14.800106 / 8.074308 (6.725798) | 13.993488 / 10.191392 (3.802096) | 0.165829 / 0.680424 (-0.514595) | 0.017347 / 0.534201 (-0.516854) | 0.401819 / 0.579283 (-0.177464) | 0.424577 / 0.434364 (-0.009787) | 0.475161 / 0.540337 (-0.065176) | 0.574659 / 1.386936 (-0.812277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n" ]

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[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.", "Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.", "> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails.", "@mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006795 / 0.011353 (-0.004558) | 0.004170 / 0.011008 (-0.006838) | 0.098698 / 0.038508 (0.060190) | 0.045393 / 0.023109 (0.022284) | 0.309205 / 0.275898 (0.033307) | 0.361333 / 0.323480 (0.037853) | 0.006009 / 0.007986 (-0.001977) | 0.003334 / 0.004328 (-0.000995) | 0.075071 / 0.004250 (0.070821) | 0.062587 / 0.037052 (0.025535) | 0.322395 / 0.258489 (0.063906) | 0.360499 / 0.293841 (0.066659) | 0.032243 / 0.128546 (-0.096303) | 0.008768 / 0.075646 (-0.066878) | 0.329799 / 0.419271 (-0.089472) | 0.062261 / 0.043533 (0.018728) | 0.298112 / 0.255139 (0.042973) | 0.322815 / 0.283200 (0.039615) | 0.032348 / 0.141683 (-0.109335) | 1.445807 / 1.452155 (-0.006347) | 1.528768 / 1.492716 (0.036051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195701 / 0.018006 (0.177695) | 0.437042 / 0.000490 (0.436552) | 0.003867 / 0.000200 (0.003667) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026713 / 0.037411 (-0.010698) | 0.109548 / 0.014526 (0.095022) | 0.119216 / 0.176557 (-0.057341) | 0.178947 / 0.737135 (-0.558188) | 0.125224 / 0.296338 (-0.171114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400885 / 0.215209 (0.185676) | 3.991223 / 2.077655 (1.913568) | 1.818449 / 1.504120 (0.314329) | 1.609285 / 1.541195 (0.068090) | 1.666675 / 1.468490 (0.198184) | 0.531486 / 4.584777 (-4.053291) | 3.770142 / 3.745712 (0.024430) | 3.057189 / 5.269862 (-2.212673) | 1.517491 / 4.565676 (-3.048186) | 0.065782 / 0.424275 (-0.358493) | 0.011251 / 0.007607 (0.003644) | 0.504277 / 0.226044 (0.278233) | 5.038979 / 2.268929 (2.770050) | 2.254717 / 55.444624 (-53.189908) | 1.929743 / 6.876477 (-4.946734) | 2.080051 / 2.142072 (-0.062022) | 0.656831 / 4.805227 (-4.148396) | 0.142860 / 6.500664 (-6.357804) | 0.063057 / 0.075469 (-0.012412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208819 / 1.841788 (-0.632969) | 14.456966 / 8.074308 (6.382658) | 12.839799 / 10.191392 (2.648407) | 0.164361 / 0.680424 (-0.516063) | 0.017330 / 0.534201 (-0.516871) | 0.397384 / 0.579283 (-0.181899) | 0.422704 / 0.434364 (-0.011660) | 0.472065 / 0.540337 (-0.068273) | 0.576960 / 1.386936 (-0.809976) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006950 / 0.011353 (-0.004403) | 0.004012 / 0.011008 (-0.006997) | 0.076050 / 0.038508 (0.037542) | 0.046646 / 0.023109 (0.023537) | 0.353813 / 0.275898 (0.077915) | 0.417111 / 0.323480 (0.093631) | 0.005422 / 0.007986 (-0.002564) | 0.003356 / 0.004328 (-0.000972) | 0.076662 / 0.004250 (0.072411) | 0.055018 / 0.037052 (0.017966) | 0.371561 / 0.258489 (0.113072) | 0.410471 / 0.293841 (0.116630) | 0.031860 / 0.128546 (-0.096686) | 0.008754 / 0.075646 (-0.066893) | 0.083192 / 0.419271 (-0.336079) | 0.050479 / 0.043533 (0.006946) | 0.351725 / 0.255139 (0.096586) | 0.371596 / 0.283200 (0.088396) | 0.023042 / 0.141683 (-0.118641) | 1.480533 / 1.452155 (0.028379) | 1.545970 / 1.492716 (0.053254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220095 / 0.018006 (0.202089) | 0.441550 / 0.000490 (0.441061) | 0.000375 / 0.000200 (0.000175) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029527 / 0.037411 (-0.007884) | 0.111645 / 0.014526 (0.097119) | 0.125732 / 0.176557 (-0.050825) | 0.177322 / 0.737135 (-0.559813) | 0.128620 / 0.296338 (-0.167718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432415 / 0.215209 (0.217206) | 4.314381 / 2.077655 (2.236726) | 2.079450 / 1.504120 (0.575331) | 1.893139 / 1.541195 (0.351944) | 1.951363 / 1.468490 (0.482873) | 0.531466 / 4.584777 (-4.053311) | 3.716860 / 3.745712 (-0.028852) | 1.850111 / 5.269862 (-3.419750) | 1.100676 / 4.565676 (-3.465000) | 0.066247 / 0.424275 (-0.358028) | 0.011503 / 0.007607 (0.003896) | 0.537208 / 0.226044 (0.311164) | 5.367560 / 2.268929 (3.098631) | 2.543697 / 55.444624 (-52.900927) | 2.221670 / 6.876477 (-4.654806) | 2.252009 / 2.142072 (0.109937) | 0.658509 / 4.805227 (-4.146718) | 0.142345 / 6.500664 (-6.358319) | 0.064701 / 0.075469 (-0.010768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266442 / 1.841788 (-0.575346) | 15.105953 / 8.074308 (7.031645) | 14.288229 / 10.191392 (4.096837) | 0.161182 / 0.680424 (-0.519242) | 0.017074 / 0.534201 (-0.517127) | 0.399464 / 0.579283 (-0.179819) | 0.419459 / 0.434364 (-0.014905) | 0.467553 / 0.540337 (-0.072784) | 0.566337 / 1.386936 (-0.820599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n" ]

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[ "i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?", "@lhoestq ", "cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset builder, and therefore contains the SparkContext itself.\r\n\r\nI think it can be fixed by defining `create_cache_and_write_probe` outside the Spark dataset builder, and pass a `partial(create_cache_and_write_probe, cache_dir=self._cache_dir)` to `mapPartitions`", "Just saw this; thanks for flagging! Your proposed solution sounds good. I can prepare a PR", "@maddiedawson can you show me the demo ,so i can test in local .before your PR" ]

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[ "Does \"number of shards\" refer to the total number of data?\r\n\r\nmy config:\r\nnproc_per_node=2\r\nds=ds['train'] = load_dataset(streaming=True).take(50000)\r\n\r\nI'm test again: in prepare_data(), data have the same for each GPU\r\n", "The number of shards is `ds.n_shards`. It corresponds generally to the number of files the dataset is made of, to be able to distribute to several nodes.\r\n\r\n**You don't end up with the same data per GPU**. But all the samples are going through your preprocessing function you pass to map. They are just skipped afterwards to only keep 1 sample out of n(GPUs)", "For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end. \r\nIs my understanding correct?\r\n\r\nWhere can I print the actual training data for each GPU?", "> For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\nIs my understanding correct?\r\n\r\nYes exactly :)\r\n\r\n> Where can I print the actual training data for each GPU?\r\n\r\nYou should call print in the data_collator", "I print out n_shards, and under multiple GPUs, this value is always 1.\r\nIs this value correct?", "Yes it's correct, and it explains why you always have the same data passed to your map function (the data can't be split).\r\n\r\nBut after being passed to `map`, each GPU keeps one example out of n(GPUs) so that you don't end up with duplicate data across GPUs", "> > For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\n> > Is my understanding correct?\r\n> \r\n> Yes exactly :)\r\n> \r\n> > Where can I print the actual training data for each GPU?\r\n> \r\n> You should call print in the data_collator\r\n\r\nOK, when printing the train data in the data collator, each GPU sees different data.\r\n\r\nThanks for your reply" ]

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[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006232 / 0.011353 (-0.005121) | 0.003788 / 0.011008 (-0.007220) | 0.100014 / 0.038508 (0.061506) | 0.036488 / 0.023109 (0.013379) | 0.306255 / 0.275898 (0.030357) | 0.363337 / 0.323480 (0.039857) | 0.004765 / 0.007986 (-0.003221) | 0.002935 / 0.004328 (-0.001394) | 0.078897 / 0.004250 (0.074647) | 0.052221 / 0.037052 (0.015169) | 0.315169 / 0.258489 (0.056680) | 0.353050 / 0.293841 (0.059209) | 0.029059 / 0.128546 (-0.099488) | 0.008599 / 0.075646 (-0.067047) | 0.318770 / 0.419271 (-0.100502) | 0.046631 / 0.043533 (0.003098) | 0.303728 / 0.255139 (0.048589) | 0.332379 / 0.283200 (0.049180) | 0.021164 / 0.141683 (-0.120519) | 1.576963 / 1.452155 (0.124808) | 1.629575 / 1.492716 (0.136859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204246 / 0.018006 (0.186240) | 0.426600 / 0.000490 (0.426110) | 0.004336 / 0.000200 (0.004136) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024039 / 0.037411 (-0.013372) | 0.098240 / 0.014526 (0.083715) | 0.108889 / 0.176557 (-0.067668) | 0.170827 / 0.737135 (-0.566308) | 0.111288 / 0.296338 (-0.185051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418103 / 0.215209 (0.202894) | 4.190759 / 2.077655 (2.113104) | 1.875978 / 1.504120 (0.371858) | 1.679198 / 1.541195 (0.138003) | 1.737965 / 1.468490 (0.269474) | 0.556660 / 4.584777 (-4.028117) | 3.413800 / 3.745712 (-0.331912) | 3.004999 / 5.269862 (-2.264862) | 1.464030 / 4.565676 (-3.101647) | 0.067338 / 0.424275 (-0.356937) | 0.011486 / 0.007607 (0.003879) | 0.522589 / 0.226044 (0.296544) | 5.214653 / 2.268929 (2.945724) | 2.316903 / 55.444624 (-53.127722) | 1.991941 / 6.876477 (-4.884536) | 2.110601 / 2.142072 (-0.031471) | 0.665400 / 4.805227 (-4.139828) | 0.135755 / 6.500664 (-6.364910) | 0.065980 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197269 / 1.841788 (-0.644519) | 14.085205 / 8.074308 (6.010897) | 14.083360 / 10.191392 (3.891968) | 0.148054 / 0.680424 (-0.532369) | 0.016548 / 0.534201 (-0.517653) | 0.371538 / 0.579283 (-0.207745) | 0.391068 / 0.434364 (-0.043296) | 0.430589 / 0.540337 (-0.109748) | 0.529319 / 1.386936 (-0.857617) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006214 / 0.011353 (-0.005138) | 0.003846 / 0.011008 (-0.007162) | 0.078559 / 0.038508 (0.040051) | 0.037855 / 0.023109 (0.014745) | 0.437479 / 0.275898 (0.161581) | 0.497588 / 0.323480 (0.174108) | 0.003491 / 0.007986 (-0.004494) | 0.003900 / 0.004328 (-0.000428) | 0.078443 / 0.004250 (0.074193) | 0.048019 / 0.037052 (0.010967) | 0.452076 / 0.258489 (0.193587) | 0.494597 / 0.293841 (0.200756) | 0.028127 / 0.128546 (-0.100419) | 0.008549 / 0.075646 (-0.067098) | 0.082977 / 0.419271 (-0.336295) | 0.043133 / 0.043533 (-0.000400) | 0.441342 / 0.255139 (0.186203) | 0.464339 / 0.283200 (0.181139) | 0.020110 / 0.141683 (-0.121573) | 1.485181 / 1.452155 (0.033026) | 1.532019 / 1.492716 (0.039302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228014 / 0.018006 (0.210007) | 0.416887 / 0.000490 (0.416397) | 0.001133 / 0.000200 (0.000933) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026452 / 0.037411 (-0.010960) | 0.104328 / 0.014526 (0.089802) | 0.110045 / 0.176557 (-0.066511) | 0.164725 / 0.737135 (-0.572410) | 0.116348 / 0.296338 (-0.179990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483502 / 0.215209 (0.268293) | 4.829814 / 2.077655 (2.752159) | 2.505271 / 1.504120 (1.001151) | 2.305819 / 1.541195 (0.764624) | 2.348633 / 1.468490 (0.880143) | 0.562316 / 4.584777 (-4.022461) | 3.426425 / 3.745712 (-0.319287) | 1.737934 / 5.269862 (-3.531927) | 1.042616 / 4.565676 (-3.523061) | 0.068088 / 0.424275 (-0.356187) | 0.011735 / 0.007607 (0.004128) | 0.586339 / 0.226044 (0.360295) | 5.861283 / 2.268929 (3.592354) | 2.953956 / 55.444624 (-52.490668) | 2.626611 / 6.876477 (-4.249865) | 2.687978 / 2.142072 (0.545906) | 0.672748 / 4.805227 (-4.132479) | 0.137231 / 6.500664 (-6.363433) | 0.068149 / 0.075469 (-0.007320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.323139 / 1.841788 (-0.518649) | 14.503102 / 8.074308 (6.428794) | 14.092102 / 10.191392 (3.900710) | 0.165395 / 0.680424 (-0.515028) | 0.016898 / 0.534201 (-0.517303) | 0.366905 / 0.579283 (-0.212378) | 0.396671 / 0.434364 (-0.037692) | 0.421831 / 0.540337 (-0.118506) | 0.514075 / 1.386936 (-0.872861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007778 / 0.011353 (-0.003575) | 0.004624 / 0.011008 (-0.006384) | 0.123426 / 0.038508 (0.084918) | 0.052209 / 0.023109 (0.029100) | 0.341084 / 0.275898 (0.065186) | 0.421905 / 0.323480 (0.098425) | 0.005768 / 0.007986 (-0.002217) | 0.003647 / 0.004328 (-0.000682) | 0.085569 / 0.004250 (0.081319) | 0.070473 / 0.037052 (0.033421) | 0.356626 / 0.258489 (0.098136) | 0.407413 / 0.293841 (0.113572) | 0.038800 / 0.128546 (-0.089746) | 0.010289 / 0.075646 (-0.065357) | 0.462707 / 0.419271 (0.043436) | 0.060390 / 0.043533 (0.016858) | 0.349805 / 0.255139 (0.094666) | 0.355288 / 0.283200 (0.072088) | 0.025364 / 0.141683 (-0.116318) | 1.745720 / 1.452155 (0.293565) | 1.852764 / 1.492716 (0.360048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290582 / 0.018006 (0.272576) | 0.480044 / 0.000490 (0.479554) | 0.007658 / 0.000200 (0.007458) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031529 / 0.037411 (-0.005882) | 0.130441 / 0.014526 (0.115915) | 0.147653 / 0.176557 (-0.028904) | 0.215935 / 0.737135 (-0.521200) | 0.149871 / 0.296338 (-0.146467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461662 / 0.215209 (0.246453) | 4.570353 / 2.077655 (2.492698) | 2.104416 / 1.504120 (0.600297) | 1.936974 / 1.541195 (0.395779) | 2.139167 / 1.468490 (0.670677) | 0.645100 / 4.584777 (-3.939677) | 4.361536 / 3.745712 (0.615824) | 2.155960 / 5.269862 (-3.113902) | 1.207854 / 4.565676 (-3.357822) | 0.080162 / 0.424275 (-0.344113) | 0.014265 / 0.007607 (0.006658) | 0.606294 / 0.226044 (0.380250) | 5.928093 / 2.268929 (3.659165) | 2.701811 / 55.444624 (-52.742813) | 2.344490 / 6.876477 (-4.531987) | 2.435997 / 2.142072 (0.293925) | 0.761020 / 4.805227 (-4.044207) | 0.165860 / 6.500664 (-6.334804) | 0.075666 / 0.075469 (0.000197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.427318 / 1.841788 (-0.414469) | 17.327468 / 8.074308 (9.253160) | 15.323065 / 10.191392 (5.131673) | 0.178518 / 0.680424 (-0.501905) | 0.020888 / 0.534201 (-0.513313) | 0.497891 / 0.579283 (-0.081393) | 0.487717 / 0.434364 (0.053353) | 0.581430 / 0.540337 (0.041093) | 0.703430 / 1.386936 (-0.683506) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007954 / 0.011353 (-0.003399) | 0.004442 / 0.011008 (-0.006566) | 0.090950 / 0.038508 (0.052442) | 0.054282 / 0.023109 (0.031173) | 0.424474 / 0.275898 (0.148576) | 0.531770 / 0.323480 (0.208290) | 0.004492 / 0.007986 (-0.003493) | 0.004745 / 0.004328 (0.000416) | 0.088213 / 0.004250 (0.083962) | 0.063967 / 0.037052 (0.026914) | 0.454256 / 0.258489 (0.195767) | 0.502870 / 0.293841 (0.209029) | 0.038203 / 0.128546 (-0.090343) | 0.010327 / 0.075646 (-0.065319) | 0.097809 / 0.419271 (-0.321463) | 0.062136 / 0.043533 (0.018604) | 0.426148 / 0.255139 (0.171009) | 0.467812 / 0.283200 (0.184612) | 0.029148 / 0.141683 (-0.112535) | 1.762307 / 1.452155 (0.310152) | 1.814238 / 1.492716 (0.321521) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195676 / 0.018006 (0.177670) | 0.475382 / 0.000490 (0.474892) | 0.003070 / 0.000200 (0.002870) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033945 / 0.037411 (-0.003466) | 0.134666 / 0.014526 (0.120140) | 0.147585 / 0.176557 (-0.028971) | 0.209472 / 0.737135 (-0.527664) | 0.154471 / 0.296338 (-0.141867) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518132 / 0.215209 (0.302923) | 5.103423 / 2.077655 (3.025768) | 2.565207 / 1.504120 (1.061087) | 2.389454 / 1.541195 (0.848259) | 2.391706 / 1.468490 (0.923216) | 0.606463 / 4.584777 (-3.978314) | 4.392227 / 3.745712 (0.646515) | 2.067121 / 5.269862 (-3.202741) | 1.217551 / 4.565676 (-3.348125) | 0.074304 / 0.424275 (-0.349971) | 0.013418 / 0.007607 (0.005811) | 0.623327 / 0.226044 (0.397282) | 6.340233 / 2.268929 (4.071304) | 3.153948 / 55.444624 (-52.290677) | 2.824548 / 6.876477 (-4.051929) | 2.938402 / 2.142072 (0.796329) | 0.774305 / 4.805227 (-4.030922) | 0.170681 / 6.500664 (-6.329983) | 0.075895 / 0.075469 (0.000426) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473491 / 1.841788 (-0.368296) | 17.372294 / 8.074308 (9.297986) | 15.550201 / 10.191392 (5.358809) | 0.191402 / 0.680424 (-0.489022) | 0.021401 / 0.534201 (-0.512800) | 0.484377 / 0.579283 (-0.094906) | 0.488844 / 0.434364 (0.054480) | 0.563336 / 0.540337 (0.022999) | 0.694210 / 1.386936 (-0.692726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.003970 / 0.011008 (-0.007038) | 0.099242 / 0.038508 (0.060734) | 0.044363 / 0.023109 (0.021254) | 0.313900 / 0.275898 (0.038002) | 0.386562 / 0.323480 (0.063082) | 0.003837 / 0.007986 (-0.004149) | 0.004203 / 0.004328 (-0.000125) | 0.076191 / 0.004250 (0.071940) | 0.058823 / 0.037052 (0.021771) | 0.333838 / 0.258489 (0.075349) | 0.368235 / 0.293841 (0.074394) | 0.030774 / 0.128546 (-0.097772) | 0.008787 / 0.075646 (-0.066860) | 0.326474 / 0.419271 (-0.092798) | 0.050903 / 0.043533 (0.007370) | 0.303928 / 0.255139 (0.048789) | 0.321532 / 0.283200 (0.038333) | 0.024162 / 0.141683 (-0.117520) | 1.479662 / 1.452155 (0.027507) | 1.520300 / 1.492716 (0.027584) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212403 / 0.018006 (0.194397) | 0.448019 / 0.000490 (0.447529) | 0.005465 / 0.000200 (0.005265) | 0.000388 / 0.000054 (0.000334) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027533 / 0.037411 (-0.009878) | 0.117477 / 0.014526 (0.102952) | 0.121182 / 0.176557 (-0.055374) | 0.181150 / 0.737135 (-0.555985) | 0.128557 / 0.296338 (-0.167782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397763 / 0.215209 (0.182554) | 3.959460 / 2.077655 (1.881805) | 1.822057 / 1.504120 (0.317937) | 1.627020 / 1.541195 (0.085826) | 1.695394 / 1.468490 (0.226904) | 0.536848 / 4.584777 (-4.047929) | 3.765205 / 3.745712 (0.019493) | 3.196300 / 5.269862 (-2.073561) | 1.623583 / 4.565676 (-2.942094) | 0.065823 / 0.424275 (-0.358452) | 0.011062 / 0.007607 (0.003455) | 0.500428 / 0.226044 (0.274384) | 5.008816 / 2.268929 (2.739888) | 2.314660 / 55.444624 (-53.129965) | 2.007429 / 6.876477 (-4.869047) | 2.141438 / 2.142072 (-0.000635) | 0.656697 / 4.805227 (-4.148530) | 0.143555 / 6.500664 (-6.357109) | 0.063928 / 0.075469 (-0.011541) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.169038 / 1.841788 (-0.672750) | 15.027186 / 8.074308 (6.952878) | 13.571484 / 10.191392 (3.380092) | 0.166437 / 0.680424 (-0.513986) | 0.017656 / 0.534201 (-0.516545) | 0.397725 / 0.579283 (-0.181558) | 0.451019 / 0.434364 (0.016655) | 0.469134 / 0.540337 (-0.071203) | 0.575885 / 1.386936 (-0.811051) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006887 / 0.011353 (-0.004465) | 0.004166 / 0.011008 (-0.006842) | 0.077137 / 0.038508 (0.038629) | 0.055631 / 0.023109 (0.032522) | 0.397658 / 0.275898 (0.121760) | 0.473981 / 0.323480 (0.150502) | 0.005365 / 0.007986 (-0.002621) | 0.003401 / 0.004328 (-0.000928) | 0.076481 / 0.004250 (0.072231) | 0.056014 / 0.037052 (0.018961) | 0.415253 / 0.258489 (0.156764) | 0.457620 / 0.293841 (0.163779) | 0.031850 / 0.128546 (-0.096696) | 0.008869 / 0.075646 (-0.066777) | 0.083475 / 0.419271 (-0.335796) | 0.049232 / 0.043533 (0.005699) | 0.392947 / 0.255139 (0.137808) | 0.417243 / 0.283200 (0.134043) | 0.024554 / 0.141683 (-0.117129) | 1.508081 / 1.452155 (0.055926) | 1.541845 / 1.492716 (0.049129) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228470 / 0.018006 (0.210464) | 0.450933 / 0.000490 (0.450443) | 0.001508 / 0.000200 (0.001308) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030189 / 0.037411 (-0.007222) | 0.118853 / 0.014526 (0.104327) | 0.124809 / 0.176557 (-0.051747) | 0.175066 / 0.737135 (-0.562069) | 0.129819 / 0.296338 (-0.166519) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451830 / 0.215209 (0.236621) | 4.505352 / 2.077655 (2.427698) | 2.309303 / 1.504120 (0.805183) | 2.120983 / 1.541195 (0.579789) | 2.198808 / 1.468490 (0.730317) | 0.543836 / 4.584777 (-4.040940) | 3.836650 / 3.745712 (0.090938) | 1.872293 / 5.269862 (-3.397568) | 1.122335 / 4.565676 (-3.443342) | 0.067463 / 0.424275 (-0.356812) | 0.012143 / 0.007607 (0.004536) | 0.553674 / 0.226044 (0.327630) | 5.572101 / 2.268929 (3.303173) | 2.772151 / 55.444624 (-52.672473) | 2.451557 / 6.876477 (-4.424920) | 2.521241 / 2.142072 (0.379169) | 0.665799 / 4.805227 (-4.139428) | 0.143842 / 6.500664 (-6.356822) | 0.065373 / 0.075469 (-0.010096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271013 / 1.841788 (-0.570775) | 15.290054 / 8.074308 (7.215746) | 14.807044 / 10.191392 (4.615652) | 0.163767 / 0.680424 (-0.516657) | 0.017383 / 0.534201 (-0.516818) | 0.393046 / 0.579283 (-0.186237) | 0.423056 / 0.434364 (-0.011308) | 0.459193 / 0.540337 (-0.081145) | 0.559964 / 1.386936 (-0.826972) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#011b75f044ef7fa6b8981ef3496615296aeb315b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006112 / 0.011353 (-0.005241) | 0.003712 / 0.011008 (-0.007297) | 0.099996 / 0.038508 (0.061488) | 0.037526 / 0.023109 (0.014417) | 0.305834 / 0.275898 (0.029936) | 0.361368 / 0.323480 (0.037888) | 0.004849 / 0.007986 (-0.003136) | 0.002912 / 0.004328 (-0.001417) | 0.077729 / 0.004250 (0.073479) | 0.053203 / 0.037052 (0.016151) | 0.318088 / 0.258489 (0.059599) | 0.371745 / 0.293841 (0.077904) | 0.029384 / 0.128546 (-0.099162) | 0.008504 / 0.075646 (-0.067142) | 0.318472 / 0.419271 (-0.100799) | 0.046043 / 0.043533 (0.002510) | 0.310418 / 0.255139 (0.055279) | 0.335044 / 0.283200 (0.051844) | 0.020364 / 0.141683 (-0.121319) | 1.503201 / 1.452155 (0.051047) | 1.556408 / 1.492716 (0.063692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210245 / 0.018006 (0.192239) | 0.418918 / 0.000490 (0.418428) | 0.002552 / 0.000200 (0.002352) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022295 / 0.037411 (-0.015116) | 0.099534 / 0.014526 (0.085008) | 0.106432 / 0.176557 (-0.070124) | 0.165110 / 0.737135 (-0.572026) | 0.109851 / 0.296338 (-0.186488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423947 / 0.215209 (0.208738) | 4.232978 / 2.077655 (2.155323) | 2.004849 / 1.504120 (0.500729) | 1.814345 / 1.541195 (0.273151) | 1.809192 / 1.468490 (0.340702) | 0.561146 / 4.584777 (-4.023631) | 3.385043 / 3.745712 (-0.360669) | 1.708265 / 5.269862 (-3.561597) | 1.030290 / 4.565676 (-3.535387) | 0.067095 / 0.424275 (-0.357180) | 0.011052 / 0.007607 (0.003445) | 0.522416 / 0.226044 (0.296371) | 5.207003 / 2.268929 (2.938075) | 2.367067 / 55.444624 (-53.077558) | 1.998705 / 6.876477 (-4.877772) | 2.068633 / 2.142072 (-0.073439) | 0.672396 / 4.805227 (-4.132831) | 0.135818 / 6.500664 (-6.364846) | 0.065229 / 0.075469 (-0.010240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187079 / 1.841788 (-0.654709) | 13.893153 / 8.074308 (5.818845) | 13.951328 / 10.191392 (3.759936) | 0.142519 / 0.680424 (-0.537905) | 0.016546 / 0.534201 (-0.517655) | 0.364008 / 0.579283 (-0.215275) | 0.385957 / 0.434364 (-0.048407) | 0.425218 / 0.540337 (-0.115120) | 0.519586 / 1.386936 (-0.867350) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005914 / 0.011353 (-0.005439) | 0.003619 / 0.011008 (-0.007389) | 0.077806 / 0.038508 (0.039298) | 0.037254 / 0.023109 (0.014144) | 0.378976 / 0.275898 (0.103078) | 0.433620 / 0.323480 (0.110140) | 0.003291 / 0.007986 (-0.004694) | 0.004523 / 0.004328 (0.000194) | 0.077604 / 0.004250 (0.073353) | 0.047493 / 0.037052 (0.010441) | 0.396027 / 0.258489 (0.137538) | 0.453345 / 0.293841 (0.159504) | 0.028170 / 0.128546 (-0.100376) | 0.008431 / 0.075646 (-0.067215) | 0.083985 / 0.419271 (-0.335286) | 0.045149 / 0.043533 (0.001617) | 0.369364 / 0.255139 (0.114225) | 0.407191 / 0.283200 (0.123991) | 0.024033 / 0.141683 (-0.117649) | 1.516838 / 1.452155 (0.064683) | 1.564260 / 1.492716 (0.071544) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200848 / 0.018006 (0.182842) | 0.407818 / 0.000490 (0.407328) | 0.003971 / 0.000200 (0.003771) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025033 / 0.037411 (-0.012378) | 0.103585 / 0.014526 (0.089059) | 0.108741 / 0.176557 (-0.067816) | 0.161061 / 0.737135 (-0.576075) | 0.112763 / 0.296338 (-0.183576) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479913 / 0.215209 (0.264704) | 4.801904 / 2.077655 (2.724249) | 2.511433 / 1.504120 (1.007313) | 2.307523 / 1.541195 (0.766328) | 2.338343 / 1.468490 (0.869853) | 0.557731 / 4.584777 (-4.027046) | 3.386261 / 3.745712 (-0.359451) | 2.999978 / 5.269862 (-2.269883) | 1.463058 / 4.565676 (-3.102619) | 0.067645 / 0.424275 (-0.356630) | 0.011224 / 0.007607 (0.003617) | 0.596854 / 0.226044 (0.370810) | 5.940946 / 2.268929 (3.672017) | 2.980194 / 55.444624 (-52.464430) | 2.634961 / 6.876477 (-4.241516) | 2.648160 / 2.142072 (0.506088) | 0.669728 / 4.805227 (-4.135499) | 0.135536 / 6.500664 (-6.365128) | 0.066865 / 0.075469 (-0.008604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.287151 / 1.841788 (-0.554637) | 14.491681 / 8.074308 (6.417373) | 14.185752 / 10.191392 (3.994360) | 0.129391 / 0.680424 (-0.551032) | 0.016650 / 0.534201 (-0.517551) | 0.380111 / 0.579283 (-0.199172) | 0.392877 / 0.434364 (-0.041487) | 0.439402 / 0.540337 (-0.100935) | 0.530865 / 1.386936 (-0.856071) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011446 / 0.011353 (0.000093) | 0.006623 / 0.011008 (-0.004386) | 0.131915 / 0.038508 (0.093407) | 0.047364 / 0.023109 (0.024255) | 0.369203 / 0.275898 (0.093305) | 0.451509 / 0.323480 (0.128029) | 0.006265 / 0.007986 (-0.001720) | 0.004072 / 0.004328 (-0.000257) | 0.098626 / 0.004250 (0.094375) | 0.079523 / 0.037052 (0.042470) | 0.406038 / 0.258489 (0.147549) | 0.450564 / 0.293841 (0.156723) | 0.050793 / 0.128546 (-0.077753) | 0.014667 / 0.075646 (-0.060979) | 0.401359 / 0.419271 (-0.017913) | 0.072299 / 0.043533 (0.028767) | 0.404456 / 0.255139 (0.149317) | 0.396223 / 0.283200 (0.113023) | 0.037048 / 0.141683 (-0.104635) | 1.869123 / 1.452155 (0.416968) | 1.953621 / 1.492716 (0.460905) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237246 / 0.018006 (0.219240) | 0.533207 / 0.000490 (0.532717) | 0.007392 / 0.000200 (0.007192) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029458 / 0.037411 (-0.007954) | 0.112438 / 0.014526 (0.097912) | 0.139115 / 0.176557 (-0.037441) | 0.215225 / 0.737135 (-0.521911) | 0.134440 / 0.296338 (-0.161898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616783 / 0.215209 (0.401574) | 6.113925 / 2.077655 (4.036270) | 2.403465 / 1.504120 (0.899345) | 1.967523 / 1.541195 (0.426329) | 2.042144 / 1.468490 (0.573654) | 0.927447 / 4.584777 (-3.657330) | 5.280413 / 3.745712 (1.534701) | 2.715335 / 5.269862 (-2.554527) | 1.755640 / 4.565676 (-2.810036) | 0.114370 / 0.424275 (-0.309905) | 0.013583 / 0.007607 (0.005976) | 0.761701 / 0.226044 (0.535657) | 7.466049 / 2.268929 (5.197120) | 3.041943 / 55.444624 (-52.402682) | 2.314477 / 6.876477 (-4.562000) | 2.469285 / 2.142072 (0.327213) | 1.216055 / 4.805227 (-3.589172) | 0.214205 / 6.500664 (-6.286459) | 0.080901 / 0.075469 (0.005432) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565185 / 1.841788 (-0.276603) | 18.387986 / 8.074308 (10.313678) | 19.665109 / 10.191392 (9.473717) | 0.226670 / 0.680424 (-0.453754) | 0.028430 / 0.534201 (-0.505771) | 0.510526 / 0.579283 (-0.068757) | 0.623178 / 0.434364 (0.188814) | 0.592039 / 0.540337 (0.051702) | 0.728462 / 1.386936 (-0.658474) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009161 / 0.011353 (-0.002192) | 0.004891 / 0.011008 (-0.006117) | 0.106502 / 0.038508 (0.067994) | 0.048234 / 0.023109 (0.025125) | 0.451173 / 0.275898 (0.175275) | 0.557948 / 0.323480 (0.234468) | 0.005350 / 0.007986 (-0.002635) | 0.004559 / 0.004328 (0.000230) | 0.110393 / 0.004250 (0.106142) | 0.060624 / 0.037052 (0.023572) | 0.459265 / 0.258489 (0.200776) | 0.575302 / 0.293841 (0.281461) | 0.051379 / 0.128546 (-0.077167) | 0.015576 / 0.075646 (-0.060070) | 0.116650 / 0.419271 (-0.302621) | 0.065534 / 0.043533 (0.022001) | 0.461431 / 0.255139 (0.206292) | 0.487677 / 0.283200 (0.204477) | 0.037773 / 0.141683 (-0.103910) | 1.992416 / 1.452155 (0.540261) | 1.991280 / 1.492716 (0.498564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233607 / 0.018006 (0.215601) | 0.507539 / 0.000490 (0.507049) | 0.001307 / 0.000200 (0.001107) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032897 / 0.037411 (-0.004514) | 0.126549 / 0.014526 (0.112023) | 0.137893 / 0.176557 (-0.038663) | 0.192124 / 0.737135 (-0.545012) | 0.147300 / 0.296338 (-0.149038) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.679371 / 0.215209 (0.464162) | 6.673249 / 2.077655 (4.595595) | 2.979141 / 1.504120 (1.475022) | 2.568789 / 1.541195 (1.027594) | 2.537540 / 1.468490 (1.069050) | 0.973555 / 4.584777 (-3.611222) | 5.313536 / 3.745712 (1.567824) | 2.693283 / 5.269862 (-2.576579) | 1.819483 / 4.565676 (-2.746194) | 0.111644 / 0.424275 (-0.312631) | 0.013218 / 0.007607 (0.005611) | 0.776114 / 0.226044 (0.550070) | 7.758907 / 2.268929 (5.489978) | 3.417611 / 55.444624 (-52.027013) | 2.859502 / 6.876477 (-4.016975) | 2.927726 / 2.142072 (0.785653) | 1.163671 / 4.805227 (-3.641556) | 0.228636 / 6.500664 (-6.272028) | 0.082077 / 0.075469 (0.006607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.746150 / 1.841788 (-0.095637) | 17.961955 / 8.074308 (9.887647) | 21.590545 / 10.191392 (11.399153) | 0.210017 / 0.680424 (-0.470406) | 0.028435 / 0.534201 (-0.505766) | 0.509253 / 0.579283 (-0.070030) | 0.606993 / 0.434364 (0.172629) | 0.587189 / 0.540337 (0.046851) | 0.684023 / 1.386936 (-0.702913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005893 / 0.011353 (-0.005460) | 0.003682 / 0.011008 (-0.007327) | 0.098358 / 0.038508 (0.059850) | 0.028130 / 0.023109 (0.005020) | 0.305960 / 0.275898 (0.030062) | 0.334869 / 0.323480 (0.011390) | 0.003522 / 0.007986 (-0.004463) | 0.003683 / 0.004328 (-0.000645) | 0.079418 / 0.004250 (0.075168) | 0.037662 / 0.037052 (0.000609) | 0.310893 / 0.258489 (0.052404) | 0.341347 / 0.293841 (0.047506) | 0.027450 / 0.128546 (-0.101096) | 0.008381 / 0.075646 (-0.067265) | 0.316020 / 0.419271 (-0.103252) | 0.045079 / 0.043533 (0.001546) | 0.307806 / 0.255139 (0.052667) | 0.331804 / 0.283200 (0.048604) | 0.091806 / 0.141683 (-0.049877) | 1.492611 / 1.452155 (0.040457) | 1.551762 / 1.492716 (0.059046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201640 / 0.018006 (0.183634) | 0.422776 / 0.000490 (0.422286) | 0.003734 / 0.000200 (0.003535) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025429 / 0.037411 (-0.011982) | 0.104699 / 0.014526 (0.090173) | 0.110505 / 0.176557 (-0.066051) | 0.171252 / 0.737135 (-0.565883) | 0.113131 / 0.296338 (-0.183208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419914 / 0.215209 (0.204705) | 4.184414 / 2.077655 (2.106760) | 1.999263 / 1.504120 (0.495143) | 1.828669 / 1.541195 (0.287474) | 1.940366 / 1.468490 (0.471876) | 0.556939 / 4.584777 (-4.027838) | 3.389164 / 3.745712 (-0.356548) | 1.796323 / 5.269862 (-3.473538) | 1.048843 / 4.565676 (-3.516833) | 0.067315 / 0.424275 (-0.356960) | 0.011531 / 0.007607 (0.003923) | 0.517226 / 0.226044 (0.291182) | 5.167255 / 2.268929 (2.898326) | 2.431129 / 55.444624 (-53.013495) | 2.133913 / 6.876477 (-4.742564) | 2.359021 / 2.142072 (0.216948) | 0.666390 / 4.805227 (-4.138838) | 0.135147 / 6.500664 (-6.365517) | 0.064855 / 0.075469 (-0.010614) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.166530 / 1.841788 (-0.675258) | 14.060551 / 8.074308 (5.986242) | 14.171663 / 10.191392 (3.980271) | 0.285821 / 0.680424 (-0.394603) | 0.016867 / 0.534201 (-0.517334) | 0.369102 / 0.579283 (-0.210181) | 0.393580 / 0.434364 (-0.040784) | 0.423721 / 0.540337 (-0.116616) | 0.512559 / 1.386936 (-0.874377) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006674 / 0.011353 (-0.004679) | 0.004006 / 0.011008 (-0.007002) | 0.080160 / 0.038508 (0.041652) | 0.032508 / 0.023109 (0.009399) | 0.378168 / 0.275898 (0.102270) | 0.417796 / 0.323480 (0.094316) | 0.003706 / 0.007986 (-0.004280) | 0.002995 / 0.004328 (-0.001333) | 0.079275 / 0.004250 (0.075025) | 0.043690 / 0.037052 (0.006638) | 0.377717 / 0.258489 (0.119228) | 0.439801 / 0.293841 (0.145961) | 0.028438 / 0.128546 (-0.100108) | 0.008661 / 0.075646 (-0.066985) | 0.085280 / 0.419271 (-0.333991) | 0.043716 / 0.043533 (0.000183) | 0.370086 / 0.255139 (0.114947) | 0.403763 / 0.283200 (0.120563) | 0.095022 / 0.141683 (-0.046661) | 1.534376 / 1.452155 (0.082221) | 1.597658 / 1.492716 (0.104942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240229 / 0.018006 (0.222223) | 0.496281 / 0.000490 (0.495792) | 0.002165 / 0.000200 (0.001965) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025330 / 0.037411 (-0.012081) | 0.102414 / 0.014526 (0.087888) | 0.112733 / 0.176557 (-0.063824) | 0.161181 / 0.737135 (-0.575955) | 0.114196 / 0.296338 (-0.182143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456808 / 0.215209 (0.241599) | 4.534937 / 2.077655 (2.457283) | 2.318834 / 1.504120 (0.814714) | 2.074085 / 1.541195 (0.532890) | 2.117409 / 1.468490 (0.648919) | 0.559110 / 4.584777 (-4.025667) | 3.371695 / 3.745712 (-0.374017) | 2.543154 / 5.269862 (-2.726708) | 1.360552 / 4.565676 (-3.205125) | 0.067602 / 0.424275 (-0.356674) | 0.011396 / 0.007607 (0.003789) | 0.561666 / 0.226044 (0.335622) | 5.607666 / 2.268929 (3.338737) | 2.802775 / 55.444624 (-52.641849) | 2.486162 / 6.876477 (-4.390315) | 2.390885 / 2.142072 (0.248813) | 0.667407 / 4.805227 (-4.137820) | 0.135948 / 6.500664 (-6.364717) | 0.067272 / 0.075469 (-0.008197) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279664 / 1.841788 (-0.562124) | 15.188099 / 8.074308 (7.113791) | 14.380355 / 10.191392 (4.188963) | 0.140344 / 0.680424 (-0.540080) | 0.016832 / 0.534201 (-0.517369) | 0.364631 / 0.579283 (-0.214652) | 0.400306 / 0.434364 (-0.034058) | 0.430793 / 0.540337 (-0.109545) | 0.525923 / 1.386936 (-0.861013) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48ca19cf1f4d1c99765a1f847c1f6b849496d99d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008502 / 0.011353 (-0.002851) | 0.005946 / 0.011008 (-0.005062) | 0.131279 / 0.038508 (0.092771) | 0.035400 / 0.023109 (0.012291) | 0.423240 / 0.275898 (0.147342) | 0.470248 / 0.323480 (0.146768) | 0.004949 / 0.007986 (-0.003037) | 0.004544 / 0.004328 (0.000215) | 0.106856 / 0.004250 (0.102605) | 0.046579 / 0.037052 (0.009527) | 0.441135 / 0.258489 (0.182646) | 0.470401 / 0.293841 (0.176561) | 0.047231 / 0.128546 (-0.081315) | 0.017278 / 0.075646 (-0.058368) | 0.401937 / 0.419271 (-0.017335) | 0.067151 / 0.043533 (0.023619) | 0.453908 / 0.255139 (0.198769) | 0.422171 / 0.283200 (0.138971) | 0.123583 / 0.141683 (-0.018100) | 1.852895 / 1.452155 (0.400740) | 1.827282 / 1.492716 (0.334566) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246419 / 0.018006 (0.228413) | 0.576930 / 0.000490 (0.576440) | 0.007511 / 0.000200 (0.007312) | 0.000165 / 0.000054 (0.000111) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032732 / 0.037411 (-0.004680) | 0.130266 / 0.014526 (0.115740) | 0.150537 / 0.176557 (-0.026019) | 0.218554 / 0.737135 (-0.518582) | 0.148572 / 0.296338 (-0.147766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598611 / 0.215209 (0.383402) | 6.181219 / 2.077655 (4.103564) | 2.473468 / 1.504120 (0.969348) | 2.206374 / 1.541195 (0.665179) | 2.216707 / 1.468490 (0.748217) | 0.981295 / 4.584777 (-3.603482) | 5.716384 / 3.745712 (1.970672) | 5.882327 / 5.269862 (0.612466) | 2.761081 / 4.565676 (-1.804595) | 0.113544 / 0.424275 (-0.310731) | 0.015131 / 0.007607 (0.007524) | 0.850939 / 0.226044 (0.624894) | 8.046611 / 2.268929 (5.777682) | 3.340542 / 55.444624 (-52.104083) | 2.673692 / 6.876477 (-4.202785) | 2.926330 / 2.142072 (0.784257) | 1.176164 / 4.805227 (-3.629064) | 0.226745 / 6.500664 (-6.273919) | 0.085910 / 0.075469 (0.010441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.483792 / 1.841788 (-0.357995) | 18.895009 / 8.074308 (10.820701) | 20.982461 / 10.191392 (10.791069) | 0.253085 / 0.680424 (-0.427339) | 0.031284 / 0.534201 (-0.502917) | 0.516569 / 0.579283 (-0.062714) | 0.635781 / 0.434364 (0.201417) | 0.604359 / 0.540337 (0.064022) | 0.725278 / 1.386936 (-0.661658) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009220 / 0.011353 (-0.002133) | 0.005792 / 0.011008 (-0.005216) | 0.099795 / 0.038508 (0.061287) | 0.033812 / 0.023109 (0.010703) | 0.459386 / 0.275898 (0.183488) | 0.518067 / 0.323480 (0.194587) | 0.005083 / 0.007986 (-0.002902) | 0.004145 / 0.004328 (-0.000183) | 0.103506 / 0.004250 (0.099255) | 0.050429 / 0.037052 (0.013377) | 0.478149 / 0.258489 (0.219660) | 0.531280 / 0.293841 (0.237440) | 0.047373 / 0.128546 (-0.081173) | 0.013647 / 0.075646 (-0.061999) | 0.115174 / 0.419271 (-0.304098) | 0.061099 / 0.043533 (0.017566) | 0.455002 / 0.255139 (0.199863) | 0.507765 / 0.283200 (0.224565) | 0.112219 / 0.141683 (-0.029464) | 1.873591 / 1.452155 (0.421436) | 1.952061 / 1.492716 (0.459345) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.283587 / 0.018006 (0.265581) | 0.587562 / 0.000490 (0.587073) | 0.001252 / 0.000200 (0.001052) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032706 / 0.037411 (-0.004705) | 0.137715 / 0.014526 (0.123189) | 0.131932 / 0.176557 (-0.044625) | 0.200042 / 0.737135 (-0.537094) | 0.159327 / 0.296338 (-0.137011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624061 / 0.215209 (0.408852) | 6.386235 / 2.077655 (4.308580) | 2.908786 / 1.504120 (1.404666) | 2.589855 / 1.541195 (1.048660) | 2.387988 / 1.468490 (0.919498) | 0.952625 / 4.584777 (-3.632152) | 5.571641 / 3.745712 (1.825929) | 2.711154 / 5.269862 (-2.558708) | 1.788015 / 4.565676 (-2.777662) | 0.104488 / 0.424275 (-0.319787) | 0.015213 / 0.007607 (0.007606) | 0.798446 / 0.226044 (0.572401) | 8.011614 / 2.268929 (5.742686) | 3.711951 / 55.444624 (-51.732673) | 2.896881 / 6.876477 (-3.979595) | 3.172116 / 2.142072 (1.030043) | 1.136816 / 4.805227 (-3.668411) | 0.239254 / 6.500664 (-6.261410) | 0.081136 / 0.075469 (0.005667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798246 / 1.841788 (-0.043542) | 19.497108 / 8.074308 (11.422800) | 23.450258 / 10.191392 (13.258866) | 0.250021 / 0.680424 (-0.430403) | 0.029138 / 0.534201 (-0.505063) | 0.532984 / 0.579283 (-0.046299) | 0.638161 / 0.434364 (0.203797) | 0.615720 / 0.540337 (0.075382) | 0.770621 / 1.386936 (-0.616315) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d8345c5f8a844ff44cfbb30cbda514ffe89bfd7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009120 / 0.011353 (-0.002233) | 0.005381 / 0.011008 (-0.005627) | 0.139719 / 0.038508 (0.101211) | 0.037229 / 0.023109 (0.014120) | 0.414633 / 0.275898 (0.138734) | 0.480313 / 0.323480 (0.156833) | 0.005027 / 0.007986 (-0.002959) | 0.005015 / 0.004328 (0.000687) | 0.108513 / 0.004250 (0.104263) | 0.056167 / 0.037052 (0.019115) | 0.407588 / 0.258489 (0.149099) | 0.518899 / 0.293841 (0.225058) | 0.048857 / 0.128546 (-0.079689) | 0.013694 / 0.075646 (-0.061952) | 0.418035 / 0.419271 (-0.001237) | 0.067755 / 0.043533 (0.024222) | 0.417740 / 0.255139 (0.162601) | 0.478622 / 0.283200 (0.195422) | 0.118290 / 0.141683 (-0.023393) | 1.901473 / 1.452155 (0.449319) | 1.978126 / 1.492716 (0.485409) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271960 / 0.018006 (0.253954) | 0.602745 / 0.000490 (0.602255) | 0.005371 / 0.000200 (0.005171) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029620 / 0.037411 (-0.007791) | 0.122402 / 0.014526 (0.107877) | 0.132645 / 0.176557 (-0.043911) | 0.212635 / 0.737135 (-0.524500) | 0.136901 / 0.296338 (-0.159438) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.644017 / 0.215209 (0.428808) | 6.597151 / 2.077655 (4.519496) | 2.454471 / 1.504120 (0.950351) | 2.151357 / 1.541195 (0.610163) | 2.290748 / 1.468490 (0.822258) | 0.970194 / 4.584777 (-3.614583) | 5.475275 / 3.745712 (1.729563) | 2.772658 / 5.269862 (-2.497204) | 1.785311 / 4.565676 (-2.780366) | 0.114503 / 0.424275 (-0.309772) | 0.015374 / 0.007607 (0.007767) | 0.768413 / 0.226044 (0.542368) | 7.956219 / 2.268929 (5.687290) | 3.272138 / 55.444624 (-52.172486) | 2.539638 / 6.876477 (-4.336839) | 2.713526 / 2.142072 (0.571454) | 1.181221 / 4.805227 (-3.624006) | 0.236327 / 6.500664 (-6.264337) | 0.089815 / 0.075469 (0.014345) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.521805 / 1.841788 (-0.319983) | 18.196529 / 8.074308 (10.122221) | 20.287324 / 10.191392 (10.095932) | 0.256959 / 0.680424 (-0.423465) | 0.028846 / 0.534201 (-0.505355) | 0.522354 / 0.579283 (-0.056929) | 0.600216 / 0.434364 (0.165852) | 0.607668 / 0.540337 (0.067331) | 0.762101 / 1.386936 (-0.624835) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009227 / 0.011353 (-0.002126) | 0.005398 / 0.011008 (-0.005610) | 0.094998 / 0.038508 (0.056490) | 0.036633 / 0.023109 (0.013524) | 0.493317 / 0.275898 (0.217419) | 0.517216 / 0.323480 (0.193736) | 0.005510 / 0.007986 (-0.002476) | 0.004249 / 0.004328 (-0.000079) | 0.107936 / 0.004250 (0.103685) | 0.050223 / 0.037052 (0.013171) | 0.580275 / 0.258489 (0.321786) | 0.551477 / 0.293841 (0.257636) | 0.048758 / 0.128546 (-0.079788) | 0.013954 / 0.075646 (-0.061692) | 0.107021 / 0.419271 (-0.312250) | 0.064416 / 0.043533 (0.020884) | 0.485225 / 0.255139 (0.230086) | 0.513862 / 0.283200 (0.230663) | 0.118848 / 0.141683 (-0.022835) | 1.755396 / 1.452155 (0.303241) | 1.970349 / 1.492716 (0.477633) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290743 / 0.018006 (0.272737) | 0.603293 / 0.000490 (0.602803) | 0.006814 / 0.000200 (0.006614) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029862 / 0.037411 (-0.007550) | 0.136530 / 0.014526 (0.122005) | 0.133728 / 0.176557 (-0.042829) | 0.194709 / 0.737135 (-0.542427) | 0.151080 / 0.296338 (-0.145258) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.649202 / 0.215209 (0.433993) | 6.637578 / 2.077655 (4.559923) | 3.040135 / 1.504120 (1.536015) | 2.671308 / 1.541195 (1.130113) | 2.722412 / 1.468490 (1.253922) | 0.953029 / 4.584777 (-3.631748) | 5.805002 / 3.745712 (2.059290) | 5.049939 / 5.269862 (-0.219922) | 2.284053 / 4.565676 (-2.281623) | 0.130399 / 0.424275 (-0.293876) | 0.014726 / 0.007607 (0.007119) | 0.932570 / 0.226044 (0.706526) | 8.576693 / 2.268929 (6.307765) | 4.032738 / 55.444624 (-51.411886) | 3.274715 / 6.876477 (-3.601762) | 3.513788 / 2.142072 (1.371716) | 1.130624 / 4.805227 (-3.674603) | 0.219597 / 6.500664 (-6.281067) | 0.081425 / 0.075469 (0.005956) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.735312 / 1.841788 (-0.106476) | 18.438587 / 8.074308 (10.364279) | 21.582310 / 10.191392 (11.390918) | 0.224040 / 0.680424 (-0.456384) | 0.027590 / 0.534201 (-0.506611) | 0.503598 / 0.579283 (-0.075685) | 0.624379 / 0.434364 (0.190015) | 0.571911 / 0.540337 (0.031574) | 0.723215 / 1.386936 (-0.663721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e40d28f2b0060a429c70827191fa5ff3ce8cf27 \"CML watermark\")\n" ]

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[ "This is the actual error:\r\n```\r\nFailed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values\r\n```\r\nWhich means some samples are incorrectly formatted.\r\n\r\nPyArrow, a storage backend that we use under the hood, requires that all the list elements have the same level of nesting (same number of dimensions) or are `None`.\r\n```python\r\nimport pyarrow as pa\r\npa.array([[1, 2, 3], 2]) # ArrowInvalid: cannot mix list and non-list, non-null values\r\npa.array([[1, 2, 3], [2]]) # works\r\n``` ", "@mariosasko \r\nI used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\nthank you for your reply.", "Our JSON loader does the following in your case:\r\n\r\n```python\r\nimport json\r\nimport pyarrow as pa\r\n\r\nwith open(file, encoding=\"utf-8\") as f:\r\n dataset = json.load(f)\r\nkeys = set().union(*[row.keys() for row in dataset])\r\nmapping = {col: [row.get(col) for row in dataset] for col in keys}\r\npa_table = pa.Table.from_pydict(mapping) # the ArrowInvalid error comes from here\r\n```\r\n\r\nSo if this code throws an error with correctly-formatted JSON, then this is an Arrow bug and should be reported in their repo.\r\n\r\n> I used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\n\r\nYou should shuffle the data to make sure that's not the case", "@mariosasko \r\nThank you.\r\nI will try again." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006374 / 0.011353 (-0.004979) | 0.004100 / 0.011008 (-0.006909) | 0.104031 / 0.038508 (0.065523) | 0.035186 / 0.023109 (0.012076) | 0.328904 / 0.275898 (0.053006) | 0.361409 / 0.323480 (0.037929) | 0.003855 / 0.007986 (-0.004130) | 0.004140 / 0.004328 (-0.000189) | 0.080406 / 0.004250 (0.076156) | 0.045658 / 0.037052 (0.008606) | 0.341133 / 0.258489 (0.082644) | 0.372688 / 0.293841 (0.078847) | 0.032025 / 0.128546 (-0.096521) | 0.008877 / 0.075646 (-0.066769) | 0.354784 / 0.419271 (-0.064488) | 0.068874 / 0.043533 (0.025341) | 0.335441 / 0.255139 (0.080302) | 0.356498 / 0.283200 (0.073298) | 0.113367 / 0.141683 (-0.028316) | 1.522458 / 1.452155 (0.070304) | 1.608046 / 1.492716 (0.115329) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231653 / 0.018006 (0.213647) | 0.446678 / 0.000490 (0.446188) | 0.003246 / 0.000200 (0.003046) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025299 / 0.037411 (-0.012112) | 0.111440 / 0.014526 (0.096914) | 0.118758 / 0.176557 (-0.057799) | 0.175037 / 0.737135 (-0.562098) | 0.124583 / 0.296338 (-0.171755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418694 / 0.215209 (0.203484) | 4.174695 / 2.077655 (2.097041) | 1.890323 / 1.504120 (0.386203) | 1.683300 / 1.541195 (0.142106) | 1.781954 / 1.468490 (0.313464) | 0.546131 / 4.584777 (-4.038645) | 3.768055 / 3.745712 (0.022343) | 1.839878 / 5.269862 (-3.429983) | 1.111877 / 4.565676 (-3.453800) | 0.068568 / 0.424275 (-0.355707) | 0.011950 / 0.007607 (0.004343) | 0.527469 / 0.226044 (0.301425) | 5.274887 / 2.268929 (3.005958) | 2.391274 / 55.444624 (-53.053351) | 2.063837 / 6.876477 (-4.812640) | 2.140627 / 2.142072 (-0.001445) | 0.681508 / 4.805227 (-4.123719) | 0.148203 / 6.500664 (-6.352461) | 0.064456 / 0.075469 (-0.011013) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221478 / 1.841788 (-0.620310) | 14.713705 / 8.074308 (6.639397) | 14.674184 / 10.191392 (4.482792) | 0.148411 / 0.680424 (-0.532012) | 0.017858 / 0.534201 (-0.516343) | 0.436166 / 0.579283 (-0.143117) | 0.437290 / 0.434364 (0.002926) | 0.521994 / 0.540337 (-0.018343) | 0.635488 / 1.386936 (-0.751448) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006108 / 0.011353 (-0.005245) | 0.003888 / 0.011008 (-0.007120) | 0.078424 / 0.038508 (0.039916) | 0.033618 / 0.023109 (0.010509) | 0.376284 / 0.275898 (0.100386) | 0.396957 / 0.323480 (0.073477) | 0.003799 / 0.007986 (-0.004187) | 0.003160 / 0.004328 (-0.001168) | 0.078358 / 0.004250 (0.074107) | 0.045597 / 0.037052 (0.008545) | 0.386396 / 0.258489 (0.127907) | 0.412985 / 0.293841 (0.119144) | 0.031610 / 0.128546 (-0.096936) | 0.008720 / 0.075646 (-0.066926) | 0.085944 / 0.419271 (-0.333328) | 0.050780 / 0.043533 (0.007247) | 0.378099 / 0.255139 (0.122960) | 0.381894 / 0.283200 (0.098694) | 0.098926 / 0.141683 (-0.042756) | 1.513842 / 1.452155 (0.061688) | 1.595040 / 1.492716 (0.102323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208169 / 0.018006 (0.190163) | 0.431653 / 0.000490 (0.431163) | 0.000935 / 0.000200 (0.000735) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029600 / 0.037411 (-0.007812) | 0.116936 / 0.014526 (0.102410) | 0.125603 / 0.176557 (-0.050953) | 0.177007 / 0.737135 (-0.560129) | 0.130602 / 0.296338 (-0.165736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457158 / 0.215209 (0.241949) | 4.563254 / 2.077655 (2.485599) | 2.303549 / 1.504120 (0.799429) | 2.107269 / 1.541195 (0.566074) | 2.130861 / 1.468490 (0.662371) | 0.548931 / 4.584777 (-4.035846) | 3.745578 / 3.745712 (-0.000134) | 1.820372 / 5.269862 (-3.449490) | 1.099316 / 4.565676 (-3.466361) | 0.068218 / 0.424275 (-0.356057) | 0.012336 / 0.007607 (0.004728) | 0.569721 / 0.226044 (0.343676) | 5.691312 / 2.268929 (3.422384) | 2.797483 / 55.444624 (-52.647141) | 2.422621 / 6.876477 (-4.453855) | 2.426187 / 2.142072 (0.284115) | 0.674777 / 4.805227 (-4.130451) | 0.144855 / 6.500664 (-6.355809) | 0.065805 / 0.075469 (-0.009664) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305078 / 1.841788 (-0.536709) | 14.874315 / 8.074308 (6.800007) | 14.541301 / 10.191392 (4.349909) | 0.175818 / 0.680424 (-0.504606) | 0.018169 / 0.534201 (-0.516032) | 0.435836 / 0.579283 (-0.143447) | 0.458397 / 0.434364 (0.024033) | 0.506232 / 0.540337 (-0.034106) | 0.605306 / 1.386936 (-0.781630) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e0c1ceab96821c7c6557482d25a9bd2078d716a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006138 / 0.011353 (-0.005215) | 0.003792 / 0.011008 (-0.007216) | 0.099417 / 0.038508 (0.060908) | 0.028739 / 0.023109 (0.005630) | 0.302835 / 0.275898 (0.026937) | 0.336397 / 0.323480 (0.012918) | 0.003537 / 0.007986 (-0.004449) | 0.002973 / 0.004328 (-0.001355) | 0.077461 / 0.004250 (0.073211) | 0.039493 / 0.037052 (0.002440) | 0.302367 / 0.258489 (0.043878) | 0.344936 / 0.293841 (0.051095) | 0.027813 / 0.128546 (-0.100733) | 0.008591 / 0.075646 (-0.067055) | 0.318975 / 0.419271 (-0.100297) | 0.045971 / 0.043533 (0.002438) | 0.301672 / 0.255139 (0.046533) | 0.328202 / 0.283200 (0.045003) | 0.091400 / 0.141683 (-0.050282) | 1.487215 / 1.452155 (0.035060) | 1.557730 / 1.492716 (0.065014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208343 / 0.018006 (0.190336) | 0.426764 / 0.000490 (0.426275) | 0.001196 / 0.000200 (0.000996) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024332 / 0.037411 (-0.013079) | 0.101861 / 0.014526 (0.087335) | 0.108669 / 0.176557 (-0.067888) | 0.172042 / 0.737135 (-0.565093) | 0.113048 / 0.296338 (-0.183290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421419 / 0.215209 (0.206210) | 4.200816 / 2.077655 (2.123162) | 1.913516 / 1.504120 (0.409396) | 1.712167 / 1.541195 (0.170972) | 1.762129 / 1.468490 (0.293639) | 0.561616 / 4.584777 (-4.023161) | 3.398122 / 3.745712 (-0.347590) | 1.744323 / 5.269862 (-3.525538) | 1.036023 / 4.565676 (-3.529653) | 0.067658 / 0.424275 (-0.356617) | 0.011145 / 0.007607 (0.003538) | 0.522803 / 0.226044 (0.296759) | 5.226245 / 2.268929 (2.957317) | 2.355148 / 55.444624 (-53.089476) | 2.014939 / 6.876477 (-4.861538) | 2.140028 / 2.142072 (-0.002044) | 0.695049 / 4.805227 (-4.110178) | 0.138428 / 6.500664 (-6.362236) | 0.066721 / 0.075469 (-0.008748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219610 / 1.841788 (-0.622177) | 14.239576 / 8.074308 (6.165268) | 14.381955 / 10.191392 (4.190563) | 0.131208 / 0.680424 (-0.549216) | 0.016698 / 0.534201 (-0.517503) | 0.361373 / 0.579283 (-0.217910) | 0.382560 / 0.434364 (-0.051804) | 0.419427 / 0.540337 (-0.120911) | 0.508314 / 1.386936 (-0.878622) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006174 / 0.011353 (-0.005179) | 0.003893 / 0.011008 (-0.007115) | 0.079614 / 0.038508 (0.041106) | 0.028685 / 0.023109 (0.005576) | 0.368627 / 0.275898 (0.092729) | 0.411599 / 0.323480 (0.088119) | 0.003573 / 0.007986 (-0.004413) | 0.002989 / 0.004328 (-0.001340) | 0.078653 / 0.004250 (0.074402) | 0.041146 / 0.037052 (0.004094) | 0.362387 / 0.258489 (0.103898) | 0.417234 / 0.293841 (0.123393) | 0.027958 / 0.128546 (-0.100589) | 0.008695 / 0.075646 (-0.066952) | 0.084637 / 0.419271 (-0.334635) | 0.044188 / 0.043533 (0.000655) | 0.358514 / 0.255139 (0.103375) | 0.392314 / 0.283200 (0.109114) | 0.093986 / 0.141683 (-0.047697) | 1.535366 / 1.452155 (0.083212) | 1.605978 / 1.492716 (0.113262) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196215 / 0.018006 (0.178209) | 0.429403 / 0.000490 (0.428913) | 0.003736 / 0.000200 (0.003536) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025281 / 0.037411 (-0.012130) | 0.104325 / 0.014526 (0.089799) | 0.111548 / 0.176557 (-0.065009) | 0.162326 / 0.737135 (-0.574809) | 0.113853 / 0.296338 (-0.182486) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447600 / 0.215209 (0.232391) | 4.463422 / 2.077655 (2.385767) | 2.168028 / 1.504120 (0.663908) | 1.968699 / 1.541195 (0.427504) | 2.035531 / 1.468490 (0.567041) | 0.564575 / 4.584777 (-4.020202) | 3.435338 / 3.745712 (-0.310374) | 2.981930 / 5.269862 (-2.287932) | 1.492172 / 4.565676 (-3.073505) | 0.067981 / 0.424275 (-0.356294) | 0.011254 / 0.007607 (0.003647) | 0.544385 / 0.226044 (0.318340) | 5.441694 / 2.268929 (3.172765) | 2.650168 / 55.444624 (-52.794456) | 2.333974 / 6.876477 (-4.542503) | 2.383424 / 2.142072 (0.241351) | 0.669814 / 4.805227 (-4.135414) | 0.135456 / 6.500664 (-6.365209) | 0.067067 / 0.075469 (-0.008402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313275 / 1.841788 (-0.528513) | 14.527636 / 8.074308 (6.453328) | 14.470957 / 10.191392 (4.279565) | 0.144361 / 0.680424 (-0.536063) | 0.016847 / 0.534201 (-0.517354) | 0.365158 / 0.579283 (-0.214125) | 0.393809 / 0.434364 (-0.040555) | 0.428527 / 0.540337 (-0.111810) | 0.515816 / 1.386936 (-0.871120) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7845d4c3c301226b3f8941ac90aaa123bfd7c69e \"CML watermark\")\n" ]

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[ "cc @sanchit-gandhi @Vaibhavs10 @lhoestq - this is mainly for demos that use Common Voice datasets as done here: https://github.com/facebookresearch/fairseq/tree/main/examples/mms#-transformers\r\n", "Hi ! the error for me is\r\n\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/mozilla-foundation/common_voice_13_0/common_voice_13_0.py or any data file in the same directory. Couldn't find 'mozilla-foundation/common_voice_13_0' on the Hugging Face Hub either: FileNotFoundError: Dataset 'mozilla-foundation/common_voice_13_0' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\nAnd tbh idk how you managed to get your error. \"n_shards.json\" is not even a thing in `datasets`", "Okay, I am able to reproduce @patrickvonplaten's original error: https://github.com/Vaibhavs10/scratchpad/blob/main/cv13_datasets_test.ipynb\r\n\r\nAlso not sure why it looks for `n_shards.json`", "Ok I see, this file is downloaded from the CV dataset script - let me investigate", "Ok I see: when you log out you no longer have access to the repository.\r\n\r\nTherefore the dataset script is loaded from cache:\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_13_0/22809012aac1fc9803eaffc44122e4149043748e93933935d5ea19898587e4d7 (last modified on Wed Jun 14 10:13:17 2023) since it couldn't be found locally at mozilla-foundation/common_voice_13_0., or remotely on the Hugging Face Hub.\r\n```\r\n\r\nand the script tries to download the n_shards.json but fails", "Is this ok for you https://github.com/huggingface/datasets/pull/5954 ?\r\n\r\nI'll do a release this afternoon", "Cool! ", "this is included in the new release 2.13.0" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006522 / 0.011353 (-0.004831) | 0.004319 / 0.011008 (-0.006690) | 0.099280 / 0.038508 (0.060772) | 0.033117 / 0.023109 (0.010007) | 0.339392 / 0.275898 (0.063494) | 0.366219 / 0.323480 (0.042739) | 0.003896 / 0.007986 (-0.004090) | 0.003412 / 0.004328 (-0.000916) | 0.076655 / 0.004250 (0.072404) | 0.045203 / 0.037052 (0.008150) | 0.355800 / 0.258489 (0.097311) | 0.372533 / 0.293841 (0.078692) | 0.032318 / 0.128546 (-0.096229) | 0.009030 / 0.075646 (-0.066616) | 0.328701 / 0.419271 (-0.090571) | 0.052891 / 0.043533 (0.009358) | 0.341131 / 0.255139 (0.085992) | 0.351593 / 0.283200 (0.068393) | 0.105136 / 0.141683 (-0.036546) | 1.475953 / 1.452155 (0.023798) | 1.566074 / 1.492716 (0.073357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216671 / 0.018006 (0.198664) | 0.446952 / 0.000490 (0.446462) | 0.006340 / 0.000200 (0.006140) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028293 / 0.037411 (-0.009118) | 0.112298 / 0.014526 (0.097773) | 0.118634 / 0.176557 (-0.057923) | 0.175542 / 0.737135 (-0.561593) | 0.124773 / 0.296338 (-0.171565) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435209 / 0.215209 (0.220000) | 4.344361 / 2.077655 (2.266706) | 2.128943 / 1.504120 (0.624823) | 1.945465 / 1.541195 (0.404271) | 2.049932 / 1.468490 (0.581442) | 0.547126 / 4.584777 (-4.037651) | 3.768698 / 3.745712 (0.022986) | 1.924441 / 5.269862 (-3.345420) | 1.146364 / 4.565676 (-3.419312) | 0.067466 / 0.424275 (-0.356809) | 0.011175 / 0.007607 (0.003568) | 0.540978 / 0.226044 (0.314933) | 5.393120 / 2.268929 (3.124191) | 2.639027 / 55.444624 (-52.805597) | 2.327216 / 6.876477 (-4.549261) | 2.500532 / 2.142072 (0.358460) | 0.679120 / 4.805227 (-4.126107) | 0.148824 / 6.500664 (-6.351840) | 0.064195 / 0.075469 (-0.011274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.158387 / 1.841788 (-0.683401) | 14.880751 / 8.074308 (6.806443) | 14.725249 / 10.191392 (4.533857) | 0.149785 / 0.680424 (-0.530639) | 0.017338 / 0.534201 (-0.516863) | 0.390980 / 0.579283 (-0.188303) | 0.425611 / 0.434364 (-0.008753) | 0.458851 / 0.540337 (-0.081487) | 0.559209 / 1.386936 (-0.827727) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006835 / 0.011353 (-0.004518) | 0.004318 / 0.011008 (-0.006690) | 0.076715 / 0.038508 (0.038207) | 0.033528 / 0.023109 (0.010419) | 0.411986 / 0.275898 (0.136087) | 0.438752 / 0.323480 (0.115272) | 0.004039 / 0.007986 (-0.003947) | 0.003509 / 0.004328 (-0.000819) | 0.077924 / 0.004250 (0.073673) | 0.049519 / 0.037052 (0.012467) | 0.420595 / 0.258489 (0.162106) | 0.450536 / 0.293841 (0.156695) | 0.032817 / 0.128546 (-0.095729) | 0.008963 / 0.075646 (-0.066684) | 0.083818 / 0.419271 (-0.335454) | 0.057591 / 0.043533 (0.014058) | 0.404605 / 0.255139 (0.149466) | 0.423661 / 0.283200 (0.140462) | 0.110698 / 0.141683 (-0.030984) | 1.512515 / 1.452155 (0.060361) | 1.569207 / 1.492716 (0.076490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200795 / 0.018006 (0.182789) | 0.448853 / 0.000490 (0.448363) | 0.003657 / 0.000200 (0.003457) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031612 / 0.037411 (-0.005799) | 0.116712 / 0.014526 (0.102186) | 0.126162 / 0.176557 (-0.050395) | 0.180522 / 0.737135 (-0.556614) | 0.129768 / 0.296338 (-0.166570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433797 / 0.215209 (0.218588) | 4.353099 / 2.077655 (2.275444) | 2.117582 / 1.504120 (0.613462) | 1.934487 / 1.541195 (0.393292) | 2.016988 / 1.468490 (0.548498) | 0.531387 / 4.584777 (-4.053390) | 3.843520 / 3.745712 (0.097807) | 1.879560 / 5.269862 (-3.390301) | 1.129445 / 4.565676 (-3.436231) | 0.065952 / 0.424275 (-0.358323) | 0.011566 / 0.007607 (0.003959) | 0.533949 / 0.226044 (0.307904) | 5.327447 / 2.268929 (3.058518) | 2.572202 / 55.444624 (-52.872422) | 2.240723 / 6.876477 (-4.635753) | 2.329290 / 2.142072 (0.187217) | 0.662162 / 4.805227 (-4.143066) | 0.143191 / 6.500664 (-6.357473) | 0.065273 / 0.075469 (-0.010196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274945 / 1.841788 (-0.566843) | 15.444511 / 8.074308 (7.370203) | 14.793524 / 10.191392 (4.602132) | 0.175607 / 0.680424 (-0.504817) | 0.017324 / 0.534201 (-0.516877) | 0.396172 / 0.579283 (-0.183111) | 0.437334 / 0.434364 (0.002970) | 0.472621 / 0.540337 (-0.067716) | 0.574888 / 1.386936 (-0.812048) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4ab1b3ed7257b0e0ad075d7271a51835f320a5e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006976 / 0.011353 (-0.004377) | 0.004541 / 0.011008 (-0.006467) | 0.106085 / 0.038508 (0.067577) | 0.029148 / 0.023109 (0.006039) | 0.306386 / 0.275898 (0.030488) | 0.351474 / 0.323480 (0.027994) | 0.003924 / 0.007986 (-0.004062) | 0.004588 / 0.004328 (0.000260) | 0.090479 / 0.004250 (0.086229) | 0.041195 / 0.037052 (0.004142) | 0.346020 / 0.258489 (0.087531) | 0.362526 / 0.293841 (0.068685) | 0.041020 / 0.128546 (-0.087526) | 0.012536 / 0.075646 (-0.063110) | 0.333247 / 0.419271 (-0.086024) | 0.059786 / 0.043533 (0.016253) | 0.318094 / 0.255139 (0.062955) | 0.343879 / 0.283200 (0.060679) | 0.110083 / 0.141683 (-0.031600) | 1.514027 / 1.452155 (0.061872) | 1.551435 / 1.492716 (0.058719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235401 / 0.018006 (0.217395) | 0.544292 / 0.000490 (0.543803) | 0.005284 / 0.000200 (0.005084) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025008 / 0.037411 (-0.012403) | 0.102235 / 0.014526 (0.087709) | 0.105523 / 0.176557 (-0.071034) | 0.180846 / 0.737135 (-0.556289) | 0.107078 / 0.296338 (-0.189261) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502374 / 0.215209 (0.287165) | 5.224254 / 2.077655 (3.146600) | 1.987193 / 1.504120 (0.483073) | 1.694680 / 1.541195 (0.153485) | 1.663907 / 1.468490 (0.195417) | 0.786470 / 4.584777 (-3.798307) | 4.977895 / 3.745712 (1.232183) | 4.713451 / 5.269862 (-0.556410) | 2.298763 / 4.565676 (-2.266913) | 0.090225 / 0.424275 (-0.334051) | 0.011427 / 0.007607 (0.003820) | 0.640686 / 0.226044 (0.414641) | 6.351727 / 2.268929 (4.082798) | 2.636912 / 55.444624 (-52.807712) | 2.075566 / 6.876477 (-4.800911) | 2.080260 / 2.142072 (-0.061812) | 0.952727 / 4.805227 (-3.852500) | 0.188651 / 6.500664 (-6.312013) | 0.068997 / 0.075469 (-0.006472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258878 / 1.841788 (-0.582910) | 15.444724 / 8.074308 (7.370416) | 17.521918 / 10.191392 (7.330526) | 0.189732 / 0.680424 (-0.490692) | 0.031084 / 0.534201 (-0.503117) | 0.445150 / 0.579283 (-0.134133) | 0.575844 / 0.434364 (0.141480) | 0.498162 / 0.540337 (-0.042176) | 0.635885 / 1.386936 (-0.751051) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007402 / 0.011353 (-0.003951) | 0.005058 / 0.011008 (-0.005950) | 0.077659 / 0.038508 (0.039151) | 0.034934 / 0.023109 (0.011825) | 0.373139 / 0.275898 (0.097241) | 0.411857 / 0.323480 (0.088377) | 0.003751 / 0.007986 (-0.004235) | 0.003634 / 0.004328 (-0.000695) | 0.075914 / 0.004250 (0.071663) | 0.037555 / 0.037052 (0.000503) | 0.387482 / 0.258489 (0.128993) | 0.434407 / 0.293841 (0.140566) | 0.040540 / 0.128546 (-0.088006) | 0.013458 / 0.075646 (-0.062189) | 0.096129 / 0.419271 (-0.323143) | 0.055369 / 0.043533 (0.011836) | 0.386564 / 0.255139 (0.131425) | 0.410417 / 0.283200 (0.127218) | 0.093265 / 0.141683 (-0.048418) | 1.432841 / 1.452155 (-0.019314) | 1.533180 / 1.492716 (0.040463) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281051 / 0.018006 (0.263045) | 0.547635 / 0.000490 (0.547146) | 0.004434 / 0.000200 (0.004234) | 0.000105 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026409 / 0.037411 (-0.011002) | 0.098586 / 0.014526 (0.084060) | 0.109223 / 0.176557 (-0.067334) | 0.165958 / 0.737135 (-0.571177) | 0.111751 / 0.296338 (-0.184587) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.542717 / 0.215209 (0.327508) | 5.530075 / 2.077655 (3.452420) | 2.351141 / 1.504120 (0.847022) | 2.021659 / 1.541195 (0.480464) | 1.964900 / 1.468490 (0.496410) | 0.819698 / 4.584777 (-3.765079) | 4.917412 / 3.745712 (1.171700) | 2.425149 / 5.269862 (-2.844712) | 1.561953 / 4.565676 (-3.003724) | 0.098417 / 0.424275 (-0.325858) | 0.012594 / 0.007607 (0.004986) | 0.717212 / 0.226044 (0.491168) | 6.994833 / 2.268929 (4.725904) | 2.997347 / 55.444624 (-52.447277) | 2.388366 / 6.876477 (-4.488111) | 2.502913 / 2.142072 (0.360841) | 1.030545 / 4.805227 (-3.774682) | 0.184844 / 6.500664 (-6.315820) | 0.076889 / 0.075469 (0.001420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.371647 / 1.841788 (-0.470141) | 15.522995 / 8.074308 (7.448687) | 17.349823 / 10.191392 (7.158431) | 0.229709 / 0.680424 (-0.450714) | 0.023303 / 0.534201 (-0.510898) | 0.413874 / 0.579283 (-0.165409) | 0.567552 / 0.434364 (0.133188) | 0.491722 / 0.540337 (-0.048615) | 0.590640 / 1.386936 (-0.796296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1911ffa5d1f58f509d04fe1ddeb9d00a63f94d5 \"CML watermark\")\n" ]

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[ "Thanks for opening https://huggingface.co/datasets/discofuse/discussions/3, let's continue the discussion over there if you don't mind", "I have posted there also sir, please check\r\n@lhoestq" ]

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[ "Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006635 / 0.011353 (-0.004718) | 0.004439 / 0.011008 (-0.006570) | 0.107831 / 0.038508 (0.069323) | 0.035664 / 0.023109 (0.012555) | 0.393733 / 0.275898 (0.117835) | 0.418336 / 0.323480 (0.094856) | 0.005739 / 0.007986 (-0.002247) | 0.005737 / 0.004328 (0.001408) | 0.079820 / 0.004250 (0.075569) | 0.045402 / 0.037052 (0.008349) | 0.396108 / 0.258489 (0.137619) | 0.422951 / 0.293841 (0.129110) | 0.030506 / 0.128546 (-0.098040) | 0.009785 / 0.075646 (-0.065861) | 0.375302 / 0.419271 (-0.043969) | 0.054355 / 0.043533 (0.010823) | 0.399652 / 0.255139 (0.144513) | 0.410825 / 0.283200 (0.127625) | 0.109238 / 0.141683 (-0.032445) | 1.687532 / 1.452155 (0.235378) | 1.736829 / 1.492716 (0.244113) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226514 / 0.018006 (0.208508) | 0.487010 / 0.000490 (0.486520) | 0.006436 / 0.000200 (0.006236) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029097 / 0.037411 (-0.008315) | 0.122979 / 0.014526 (0.108453) | 0.129454 / 0.176557 (-0.047103) | 0.194006 / 0.737135 (-0.543129) | 0.137968 / 0.296338 (-0.158370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466425 / 0.215209 (0.251216) | 4.627307 / 2.077655 (2.549652) | 2.108840 / 1.504120 (0.604720) | 1.882547 / 1.541195 (0.341353) | 1.891077 / 1.468490 (0.422587) | 0.590646 / 4.584777 (-3.994131) | 4.176918 / 3.745712 (0.431205) | 2.071475 / 5.269862 (-3.198386) | 1.173815 / 4.565676 (-3.391862) | 0.075330 / 0.424275 (-0.348945) | 0.012944 / 0.007607 (0.005337) | 0.587080 / 0.226044 (0.361036) | 5.827053 / 2.268929 (3.558125) | 2.694258 / 55.444624 (-52.750366) | 2.276997 / 6.876477 (-4.599480) | 2.329678 / 2.142072 (0.187605) | 0.721860 / 4.805227 (-4.083367) | 0.159238 / 6.500664 (-6.341426) | 0.073013 / 0.075469 (-0.002456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345396 / 1.841788 (-0.496391) | 16.619283 / 8.074308 (8.544975) | 14.754754 / 10.191392 (4.563362) | 0.180784 / 0.680424 (-0.499639) | 0.020376 / 0.534201 (-0.513825) | 0.451010 / 0.579283 (-0.128273) | 0.481524 / 0.434364 (0.047160) | 0.564777 / 0.540337 (0.024440) | 0.683232 / 1.386936 (-0.703704) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007243 / 0.011353 (-0.004110) | 0.005262 / 0.011008 (-0.005746) | 0.084090 / 0.038508 (0.045581) | 0.037429 / 0.023109 (0.014320) | 0.404038 / 0.275898 (0.128140) | 0.445040 / 0.323480 (0.121560) | 0.006220 / 0.007986 (-0.001766) | 0.004256 / 0.004328 (-0.000072) | 0.083794 / 0.004250 (0.079544) | 0.052655 / 0.037052 (0.015603) | 0.414083 / 0.258489 (0.155594) | 0.458190 / 0.293841 (0.164349) | 0.032719 / 0.128546 (-0.095828) | 0.010063 / 0.075646 (-0.065583) | 0.092281 / 0.419271 (-0.326990) | 0.053888 / 0.043533 (0.010355) | 0.407813 / 0.255139 (0.152674) | 0.431692 / 0.283200 (0.148493) | 0.119799 / 0.141683 (-0.021884) | 1.709853 / 1.452155 (0.257698) | 1.771592 / 1.492716 (0.278876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246540 / 0.018006 (0.228534) | 0.483199 / 0.000490 (0.482709) | 0.002514 / 0.000200 (0.002315) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031576 / 0.037411 (-0.005835) | 0.130020 / 0.014526 (0.115495) | 0.140285 / 0.176557 (-0.036272) | 0.196164 / 0.737135 (-0.540972) | 0.143924 / 0.296338 (-0.152414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488549 / 0.215209 (0.273340) | 4.888055 / 2.077655 (2.810400) | 2.389163 / 1.504120 (0.885043) | 2.184626 / 1.541195 (0.643431) | 2.260227 / 1.468490 (0.791737) | 0.601331 / 4.584777 (-3.983446) | 4.386159 / 3.745712 (0.640447) | 3.345814 / 5.269862 (-1.924048) | 1.734360 / 4.565676 (-2.831317) | 0.073199 / 0.424275 (-0.351076) | 0.012397 / 0.007607 (0.004790) | 0.601411 / 0.226044 (0.375366) | 6.135000 / 2.268929 (3.866072) | 2.930169 / 55.444624 (-52.514456) | 2.532631 / 6.876477 (-4.343845) | 2.619351 / 2.142072 (0.477279) | 0.740954 / 4.805227 (-4.064274) | 0.162936 / 6.500664 (-6.337728) | 0.073885 / 0.075469 (-0.001585) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502493 / 1.841788 (-0.339294) | 17.026756 / 8.074308 (8.952448) | 15.880958 / 10.191392 (5.689566) | 0.167261 / 0.680424 (-0.513163) | 0.020347 / 0.534201 (-0.513854) | 0.452902 / 0.579283 (-0.126381) | 0.481614 / 0.434364 (0.047250) | 0.539893 / 0.540337 (-0.000445) | 0.653401 / 1.386936 (-0.733535) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008268 / 0.011353 (-0.003084) | 0.005538 / 0.011008 (-0.005470) | 0.126136 / 0.038508 (0.087628) | 0.046100 / 0.023109 (0.022991) | 0.366882 / 0.275898 (0.090984) | 0.408912 / 0.323480 (0.085432) | 0.007090 / 0.007986 (-0.000895) | 0.004820 / 0.004328 (0.000491) | 0.091432 / 0.004250 (0.087181) | 0.058390 / 0.037052 (0.021338) | 0.368787 / 0.258489 (0.110298) | 0.419429 / 0.293841 (0.125588) | 0.034958 / 0.128546 (-0.093588) | 0.010526 / 0.075646 (-0.065120) | 0.463063 / 0.419271 (0.043791) | 0.070544 / 0.043533 (0.027011) | 0.366182 / 0.255139 (0.111043) | 0.390851 / 0.283200 (0.107652) | 0.128377 / 0.141683 (-0.013306) | 1.819385 / 1.452155 (0.367231) | 1.928834 / 1.492716 (0.436117) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228413 / 0.018006 (0.210407) | 0.485511 / 0.000490 (0.485021) | 0.005395 / 0.000200 (0.005195) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035209 / 0.037411 (-0.002203) | 0.144492 / 0.014526 (0.129967) | 0.150467 / 0.176557 (-0.026089) | 0.223861 / 0.737135 (-0.513274) | 0.156363 / 0.296338 (-0.139975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517751 / 0.215209 (0.302542) | 5.150438 / 2.077655 (3.072783) | 2.483601 / 1.504120 (0.979481) | 2.279786 / 1.541195 (0.738592) | 2.374510 / 1.468490 (0.906020) | 0.637547 / 4.584777 (-3.947230) | 4.845393 / 3.745712 (1.099681) | 2.241554 / 5.269862 (-3.028307) | 1.290105 / 4.565676 (-3.275572) | 0.079791 / 0.424275 (-0.344484) | 0.014915 / 0.007607 (0.007308) | 0.640468 / 0.226044 (0.414423) | 6.394810 / 2.268929 (4.125881) | 3.012748 / 55.444624 (-52.431876) | 2.625565 / 6.876477 (-4.250912) | 2.792435 / 2.142072 (0.650363) | 0.782284 / 4.805227 (-4.022944) | 0.171628 / 6.500664 (-6.329036) | 0.081714 / 0.075469 (0.006245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.592411 / 1.841788 (-0.249377) | 18.999604 / 8.074308 (10.925295) | 18.469946 / 10.191392 (8.278554) | 0.200878 / 0.680424 (-0.479546) | 0.021595 / 0.534201 (-0.512606) | 0.519247 / 0.579283 (-0.060036) | 0.534940 / 0.434364 (0.100576) | 0.656325 / 0.540337 (0.115987) | 0.789658 / 1.386936 (-0.597278) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008093 / 0.011353 (-0.003260) | 0.005524 / 0.011008 (-0.005484) | 0.092339 / 0.038508 (0.053831) | 0.045619 / 0.023109 (0.022510) | 0.449376 / 0.275898 (0.173478) | 0.478587 / 0.323480 (0.155107) | 0.006978 / 0.007986 (-0.001007) | 0.004622 / 0.004328 (0.000294) | 0.090618 / 0.004250 (0.086368) | 0.059321 / 0.037052 (0.022269) | 0.450989 / 0.258489 (0.192500) | 0.491652 / 0.293841 (0.197811) | 0.033308 / 0.128546 (-0.095238) | 0.010677 / 0.075646 (-0.064969) | 0.099836 / 0.419271 (-0.319435) | 0.055937 / 0.043533 (0.012404) | 0.440560 / 0.255139 (0.185421) | 0.475305 / 0.283200 (0.192105) | 0.130829 / 0.141683 (-0.010854) | 1.857943 / 1.452155 (0.405789) | 1.989534 / 1.492716 (0.496818) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244715 / 0.018006 (0.226709) | 0.482866 / 0.000490 (0.482377) | 0.001100 / 0.000200 (0.000900) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036288 / 0.037411 (-0.001124) | 0.147903 / 0.014526 (0.133377) | 0.154141 / 0.176557 (-0.022416) | 0.221863 / 0.737135 (-0.515272) | 0.162319 / 0.296338 (-0.134019) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.536972 / 0.215209 (0.321763) | 5.382866 / 2.077655 (3.305211) | 2.719575 / 1.504120 (1.215456) | 2.516596 / 1.541195 (0.975401) | 2.699602 / 1.468490 (1.231112) | 0.639886 / 4.584777 (-3.944891) | 5.109746 / 3.745712 (1.364034) | 2.260206 / 5.269862 (-3.009656) | 1.305506 / 4.565676 (-3.260170) | 0.080262 / 0.424275 (-0.344013) | 0.014801 / 0.007607 (0.007194) | 0.661228 / 0.226044 (0.435184) | 6.596485 / 2.268929 (4.327557) | 3.226114 / 55.444624 (-52.218510) | 2.859776 / 6.876477 (-4.016701) | 3.059355 / 2.142072 (0.917282) | 0.793413 / 4.805227 (-4.011814) | 0.176521 / 6.500664 (-6.324143) | 0.084062 / 0.075469 (0.008593) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.642085 / 1.841788 (-0.199703) | 20.355459 / 8.074308 (12.281151) | 17.979620 / 10.191392 (7.788228) | 0.229329 / 0.680424 (-0.451094) | 0.025681 / 0.534201 (-0.508520) | 0.534142 / 0.579283 (-0.045141) | 0.623439 / 0.434364 (0.189075) | 0.621938 / 0.540337 (0.081601) | 0.759038 / 1.386936 (-0.627898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007703 / 0.011353 (-0.003649) | 0.005362 / 0.011008 (-0.005646) | 0.113111 / 0.038508 (0.074602) | 0.038891 / 0.023109 (0.015782) | 0.348938 / 0.275898 (0.073040) | 0.398079 / 0.323480 (0.074599) | 0.006707 / 0.007986 (-0.001278) | 0.004489 / 0.004328 (0.000160) | 0.087194 / 0.004250 (0.082943) | 0.054268 / 0.037052 (0.017216) | 0.359949 / 0.258489 (0.101460) | 0.402959 / 0.293841 (0.109118) | 0.032508 / 0.128546 (-0.096038) | 0.010224 / 0.075646 (-0.065422) | 0.387007 / 0.419271 (-0.032264) | 0.058971 / 0.043533 (0.015439) | 0.345085 / 0.255139 (0.089946) | 0.384306 / 0.283200 (0.101107) | 0.122253 / 0.141683 (-0.019430) | 1.706353 / 1.452155 (0.254199) | 1.840780 / 1.492716 (0.348063) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254374 / 0.018006 (0.236368) | 0.497387 / 0.000490 (0.496897) | 0.012294 / 0.000200 (0.012094) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030902 / 0.037411 (-0.006509) | 0.132098 / 0.014526 (0.117573) | 0.140311 / 0.176557 (-0.036245) | 0.205887 / 0.737135 (-0.531249) | 0.143992 / 0.296338 (-0.152347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467367 / 0.215209 (0.252158) | 4.669936 / 2.077655 (2.592281) | 2.155358 / 1.504120 (0.651238) | 1.984132 / 1.541195 (0.442937) | 2.102352 / 1.468490 (0.633861) | 0.607014 / 4.584777 (-3.977763) | 4.396479 / 3.745712 (0.650767) | 4.666056 / 5.269862 (-0.603806) | 2.176649 / 4.565676 (-2.389028) | 0.072657 / 0.424275 (-0.351619) | 0.012367 / 0.007607 (0.004759) | 0.569706 / 0.226044 (0.343661) | 5.749083 / 2.268929 (3.480154) | 2.640824 / 55.444624 (-52.803801) | 2.310253 / 6.876477 (-4.566224) | 2.486748 / 2.142072 (0.344676) | 0.737891 / 4.805227 (-4.067336) | 0.163507 / 6.500664 (-6.337157) | 0.075776 / 0.075469 (0.000307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362710 / 1.841788 (-0.479078) | 17.010705 / 8.074308 (8.936396) | 15.084231 / 10.191392 (4.892839) | 0.218274 / 0.680424 (-0.462150) | 0.019555 / 0.534201 (-0.514646) | 0.456013 / 0.579283 (-0.123270) | 0.502772 / 0.434364 (0.068408) | 0.581480 / 0.540337 (0.041142) | 0.686952 / 1.386936 (-0.699984) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007976 / 0.011353 (-0.003377) | 0.005141 / 0.011008 (-0.005868) | 0.086629 / 0.038508 (0.048121) | 0.039553 / 0.023109 (0.016444) | 0.433028 / 0.275898 (0.157130) | 0.463444 / 0.323480 (0.139964) | 0.006967 / 0.007986 (-0.001018) | 0.005814 / 0.004328 (0.001485) | 0.086266 / 0.004250 (0.082015) | 0.055384 / 0.037052 (0.018332) | 0.428733 / 0.258489 (0.170243) | 0.475670 / 0.293841 (0.181829) | 0.032872 / 0.128546 (-0.095674) | 0.010664 / 0.075646 (-0.064983) | 0.094357 / 0.419271 (-0.324915) | 0.058386 / 0.043533 (0.014854) | 0.431114 / 0.255139 (0.175975) | 0.441728 / 0.283200 (0.158528) | 0.131942 / 0.141683 (-0.009740) | 1.782214 / 1.452155 (0.330060) | 1.843185 / 1.492716 (0.350469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247047 / 0.018006 (0.229041) | 0.488931 / 0.000490 (0.488441) | 0.002657 / 0.000200 (0.002457) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033893 / 0.037411 (-0.003518) | 0.131021 / 0.014526 (0.116495) | 0.142892 / 0.176557 (-0.033665) | 0.200955 / 0.737135 (-0.536180) | 0.151329 / 0.296338 (-0.145010) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521138 / 0.215209 (0.305929) | 5.085207 / 2.077655 (3.007552) | 2.652901 / 1.504120 (1.148781) | 2.401545 / 1.541195 (0.860350) | 2.553461 / 1.468490 (1.084971) | 0.615347 / 4.584777 (-3.969430) | 4.448038 / 3.745712 (0.702326) | 2.049997 / 5.269862 (-3.219865) | 1.190602 / 4.565676 (-3.375075) | 0.073356 / 0.424275 (-0.350919) | 0.013685 / 0.007607 (0.006078) | 0.626705 / 0.226044 (0.400660) | 6.391941 / 2.268929 (4.123012) | 3.218864 / 55.444624 (-52.225760) | 2.858808 / 6.876477 (-4.017669) | 3.005808 / 2.142072 (0.863736) | 0.740725 / 4.805227 (-4.064502) | 0.161904 / 6.500664 (-6.338760) | 0.073727 / 0.075469 (-0.001742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.488623 / 1.841788 (-0.353164) | 17.584367 / 8.074308 (9.510059) | 16.281818 / 10.191392 (6.090426) | 0.164482 / 0.680424 (-0.515942) | 0.020197 / 0.534201 (-0.514003) | 0.456750 / 0.579283 (-0.122533) | 0.501156 / 0.434364 (0.066792) | 0.549779 / 0.540337 (0.009442) | 0.650156 / 1.386936 (-0.736780) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008337 / 0.011353 (-0.003016) | 0.005911 / 0.011008 (-0.005097) | 0.129037 / 0.038508 (0.090529) | 0.046071 / 0.023109 (0.022962) | 0.418657 / 0.275898 (0.142759) | 0.490340 / 0.323480 (0.166860) | 0.006387 / 0.007986 (-0.001598) | 0.004724 / 0.004328 (0.000396) | 0.097953 / 0.004250 (0.093702) | 0.069025 / 0.037052 (0.031972) | 0.431178 / 0.258489 (0.172689) | 0.458363 / 0.293841 (0.164522) | 0.049341 / 0.128546 (-0.079205) | 0.014637 / 0.075646 (-0.061009) | 0.439800 / 0.419271 (0.020529) | 0.069905 / 0.043533 (0.026373) | 0.406775 / 0.255139 (0.151636) | 0.441989 / 0.283200 (0.158790) | 0.046009 / 0.141683 (-0.095674) | 1.847630 / 1.452155 (0.395475) | 1.904067 / 1.492716 (0.411351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288305 / 0.018006 (0.270299) | 0.594547 / 0.000490 (0.594058) | 0.005600 / 0.000200 (0.005400) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033847 / 0.037411 (-0.003564) | 0.125139 / 0.014526 (0.110613) | 0.147982 / 0.176557 (-0.028574) | 0.208396 / 0.737135 (-0.528739) | 0.144005 / 0.296338 (-0.152334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669175 / 0.215209 (0.453966) | 6.605289 / 2.077655 (4.527634) | 2.720468 / 1.504120 (1.216348) | 2.341355 / 1.541195 (0.800160) | 2.402069 / 1.468490 (0.933578) | 0.939303 / 4.584777 (-3.645474) | 5.718545 / 3.745712 (1.972833) | 2.856235 / 5.269862 (-2.413627) | 1.821555 / 4.565676 (-2.744121) | 0.105473 / 0.424275 (-0.318802) | 0.014490 / 0.007607 (0.006883) | 0.774349 / 0.226044 (0.548305) | 8.065048 / 2.268929 (5.796120) | 3.508482 / 55.444624 (-51.936143) | 2.822881 / 6.876477 (-4.053596) | 2.962947 / 2.142072 (0.820875) | 1.138944 / 4.805227 (-3.666284) | 0.248414 / 6.500664 (-6.252250) | 0.095665 / 0.075469 (0.020196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.688231 / 1.841788 (-0.153557) | 18.673305 / 8.074308 (10.598997) | 22.768663 / 10.191392 (12.577271) | 0.211238 / 0.680424 (-0.469186) | 0.031380 / 0.534201 (-0.502821) | 0.517175 / 0.579283 (-0.062108) | 0.626437 / 0.434364 (0.192073) | 0.624225 / 0.540337 (0.083888) | 0.743746 / 1.386936 (-0.643191) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008888 / 0.011353 (-0.002464) | 0.005491 / 0.011008 (-0.005517) | 0.105013 / 0.038508 (0.066505) | 0.049456 / 0.023109 (0.026347) | 0.528989 / 0.275898 (0.253091) | 0.651871 / 0.323480 (0.328391) | 0.006683 / 0.007986 (-0.001302) | 0.004365 / 0.004328 (0.000037) | 0.098161 / 0.004250 (0.093911) | 0.075615 / 0.037052 (0.038563) | 0.543746 / 0.258489 (0.285257) | 0.650855 / 0.293841 (0.357014) | 0.050220 / 0.128546 (-0.078327) | 0.014471 / 0.075646 (-0.061175) | 0.115903 / 0.419271 (-0.303368) | 0.065925 / 0.043533 (0.022392) | 0.527797 / 0.255139 (0.272658) | 0.543834 / 0.283200 (0.260634) | 0.043005 / 0.141683 (-0.098678) | 1.842846 / 1.452155 (0.390691) | 1.970615 / 1.492716 (0.477899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287350 / 0.018006 (0.269343) | 0.591139 / 0.000490 (0.590649) | 0.006423 / 0.000200 (0.006223) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034594 / 0.037411 (-0.002818) | 0.137155 / 0.014526 (0.122629) | 0.154662 / 0.176557 (-0.021894) | 0.217834 / 0.737135 (-0.519301) | 0.159642 / 0.296338 (-0.136696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664288 / 0.215209 (0.449079) | 6.926912 / 2.077655 (4.849257) | 3.028957 / 1.504120 (1.524837) | 2.625178 / 1.541195 (1.083983) | 2.725316 / 1.468490 (1.256826) | 1.015715 / 4.584777 (-3.569062) | 5.834694 / 3.745712 (2.088982) | 5.105269 / 5.269862 (-0.164593) | 2.316194 / 4.565676 (-2.249483) | 0.113802 / 0.424275 (-0.310473) | 0.014079 / 0.007607 (0.006472) | 0.893727 / 0.226044 (0.667683) | 8.577701 / 2.268929 (6.308772) | 3.706907 / 55.444624 (-51.737717) | 3.087530 / 6.876477 (-3.788947) | 3.295004 / 2.142072 (1.152931) | 1.204172 / 4.805227 (-3.601055) | 0.248720 / 6.500664 (-6.251944) | 0.107208 / 0.075469 (0.031739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.800058 / 1.841788 (-0.041730) | 19.253646 / 8.074308 (11.179338) | 22.590804 / 10.191392 (12.399412) | 0.270687 / 0.680424 (-0.409737) | 0.028678 / 0.534201 (-0.505522) | 0.534670 / 0.579283 (-0.044613) | 0.642881 / 0.434364 (0.208518) | 0.615521 / 0.540337 (0.075184) | 0.723733 / 1.386936 (-0.663203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2591cd45a002a06bd551343ec785abf16f1433e2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.017236 / 0.011353 (0.005883) | 0.005341 / 0.011008 (-0.005667) | 0.131471 / 0.038508 (0.092963) | 0.048868 / 0.023109 (0.025758) | 0.448942 / 0.275898 (0.173044) | 0.498721 / 0.323480 (0.175241) | 0.006825 / 0.007986 (-0.001161) | 0.004587 / 0.004328 (0.000259) | 0.104142 / 0.004250 (0.099891) | 0.075521 / 0.037052 (0.038469) | 0.439538 / 0.258489 (0.181049) | 0.498720 / 0.293841 (0.204879) | 0.051352 / 0.128546 (-0.077194) | 0.015070 / 0.075646 (-0.060576) | 0.441752 / 0.419271 (0.022480) | 0.089166 / 0.043533 (0.045633) | 0.428909 / 0.255139 (0.173770) | 0.446648 / 0.283200 (0.163448) | 0.042371 / 0.141683 (-0.099312) | 1.993948 / 1.452155 (0.541793) | 2.065756 / 1.492716 (0.573039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257279 / 0.018006 (0.239273) | 0.575453 / 0.000490 (0.574964) | 0.004120 / 0.000200 (0.003920) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034012 / 0.037411 (-0.003399) | 0.141737 / 0.014526 (0.127211) | 0.145241 / 0.176557 (-0.031316) | 0.226196 / 0.737135 (-0.510939) | 0.149526 / 0.296338 (-0.146813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665762 / 0.215209 (0.450553) | 6.683737 / 2.077655 (4.606083) | 2.869485 / 1.504120 (1.365365) | 2.462808 / 1.541195 (0.921613) | 2.526808 / 1.468490 (1.058318) | 0.957518 / 4.584777 (-3.627259) | 5.926261 / 3.745712 (2.180548) | 5.027822 / 5.269862 (-0.242040) | 2.643185 / 4.565676 (-1.922491) | 0.117014 / 0.424275 (-0.307261) | 0.015142 / 0.007607 (0.007535) | 0.835694 / 0.226044 (0.609650) | 8.427356 / 2.268929 (6.158427) | 3.649597 / 55.444624 (-51.795027) | 2.989607 / 6.876477 (-3.886870) | 3.043160 / 2.142072 (0.901088) | 1.158872 / 4.805227 (-3.646355) | 0.240456 / 6.500664 (-6.260208) | 0.089196 / 0.075469 (0.013726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.689361 / 1.841788 (-0.152427) | 18.842158 / 8.074308 (10.767850) | 22.604249 / 10.191392 (12.412857) | 0.248487 / 0.680424 (-0.431936) | 0.029668 / 0.534201 (-0.504533) | 0.536283 / 0.579283 (-0.043001) | 0.663253 / 0.434364 (0.228890) | 0.622973 / 0.540337 (0.082635) | 0.735297 / 1.386936 (-0.651639) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009296 / 0.011353 (-0.002057) | 0.005955 / 0.011008 (-0.005053) | 0.105723 / 0.038508 (0.067215) | 0.051184 / 0.023109 (0.028074) | 0.527095 / 0.275898 (0.251197) | 0.631697 / 0.323480 (0.308217) | 0.006577 / 0.007986 (-0.001408) | 0.004452 / 0.004328 (0.000124) | 0.105921 / 0.004250 (0.101670) | 0.071951 / 0.037052 (0.034899) | 0.572518 / 0.258489 (0.314029) | 0.623957 / 0.293841 (0.330116) | 0.050861 / 0.128546 (-0.077686) | 0.014897 / 0.075646 (-0.060749) | 0.122013 / 0.419271 (-0.297258) | 0.067194 / 0.043533 (0.023661) | 0.530352 / 0.255139 (0.275213) | 0.563912 / 0.283200 (0.280712) | 0.034756 / 0.141683 (-0.106927) | 1.961580 / 1.452155 (0.509425) | 2.052412 / 1.492716 (0.559696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304996 / 0.018006 (0.286990) | 0.584899 / 0.000490 (0.584409) | 0.010444 / 0.000200 (0.010244) | 0.000134 / 0.000054 (0.000080) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032540 / 0.037411 (-0.004871) | 0.137349 / 0.014526 (0.122823) | 0.146233 / 0.176557 (-0.030323) | 0.206978 / 0.737135 (-0.530157) | 0.154380 / 0.296338 (-0.141959) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.705438 / 0.215209 (0.490229) | 7.042159 / 2.077655 (4.964504) | 3.285501 / 1.504120 (1.781381) | 2.904710 / 1.541195 (1.363515) | 2.952838 / 1.468490 (1.484348) | 0.987784 / 4.584777 (-3.596993) | 5.949550 / 3.745712 (2.203838) | 2.927148 / 5.269862 (-2.342714) | 1.870054 / 4.565676 (-2.695622) | 0.119548 / 0.424275 (-0.304727) | 0.014565 / 0.007607 (0.006958) | 0.858311 / 0.226044 (0.632266) | 8.721679 / 2.268929 (6.452750) | 4.100825 / 55.444624 (-51.343800) | 3.358093 / 6.876477 (-3.518383) | 3.499637 / 2.142072 (1.357564) | 1.208932 / 4.805227 (-3.596295) | 0.232961 / 6.500664 (-6.267703) | 0.089727 / 0.075469 (0.014258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.780143 / 1.841788 (-0.061645) | 19.074991 / 8.074308 (11.000683) | 21.218487 / 10.191392 (11.027095) | 0.258690 / 0.680424 (-0.421734) | 0.029514 / 0.534201 (-0.504687) | 0.541764 / 0.579283 (-0.037519) | 0.640603 / 0.434364 (0.206239) | 0.635336 / 0.540337 (0.094999) | 0.756309 / 1.386936 (-0.630627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b525c199e6352aa8aac55f1dcddeb55a80db373 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009619 / 0.011353 (-0.001734) | 0.005683 / 0.011008 (-0.005325) | 0.136971 / 0.038508 (0.098463) | 0.051607 / 0.023109 (0.028497) | 0.439716 / 0.275898 (0.163818) | 0.486193 / 0.323480 (0.162713) | 0.006304 / 0.007986 (-0.001681) | 0.004489 / 0.004328 (0.000160) | 0.103837 / 0.004250 (0.099587) | 0.082954 / 0.037052 (0.045901) | 0.447286 / 0.258489 (0.188797) | 0.495434 / 0.293841 (0.201593) | 0.049244 / 0.128546 (-0.079302) | 0.015176 / 0.075646 (-0.060470) | 0.444406 / 0.419271 (0.025134) | 0.074766 / 0.043533 (0.031233) | 0.438585 / 0.255139 (0.183446) | 0.438232 / 0.283200 (0.155032) | 0.043372 / 0.141683 (-0.098311) | 2.057286 / 1.452155 (0.605131) | 2.049540 / 1.492716 (0.556824) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298038 / 0.018006 (0.280031) | 0.630771 / 0.000490 (0.630281) | 0.008287 / 0.000200 (0.008087) | 0.000123 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033637 / 0.037411 (-0.003775) | 0.128327 / 0.014526 (0.113801) | 0.150672 / 0.176557 (-0.025885) | 0.228521 / 0.737135 (-0.508614) | 0.142733 / 0.296338 (-0.153606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629072 / 0.215209 (0.413863) | 6.612047 / 2.077655 (4.534392) | 2.715594 / 1.504120 (1.211474) | 2.327823 / 1.541195 (0.786628) | 2.417508 / 1.468490 (0.949018) | 0.959134 / 4.584777 (-3.625643) | 5.669921 / 3.745712 (1.924209) | 2.977920 / 5.269862 (-2.291941) | 1.814564 / 4.565676 (-2.751112) | 0.120233 / 0.424275 (-0.304042) | 0.015859 / 0.007607 (0.008252) | 0.822618 / 0.226044 (0.596574) | 8.440306 / 2.268929 (6.171377) | 3.721611 / 55.444624 (-51.723013) | 2.954867 / 6.876477 (-3.921610) | 3.135364 / 2.142072 (0.993292) | 1.226475 / 4.805227 (-3.578752) | 0.246658 / 6.500664 (-6.254006) | 0.093920 / 0.075469 (0.018451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.665631 / 1.841788 (-0.176157) | 19.136369 / 8.074308 (11.062061) | 23.659564 / 10.191392 (13.468172) | 0.273430 / 0.680424 (-0.406994) | 0.028180 / 0.534201 (-0.506021) | 0.559588 / 0.579283 (-0.019695) | 0.649203 / 0.434364 (0.214840) | 0.647113 / 0.540337 (0.106776) | 0.737978 / 1.386936 (-0.648958) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009104 / 0.011353 (-0.002249) | 0.006838 / 0.011008 (-0.004171) | 0.104516 / 0.038508 (0.066008) | 0.047986 / 0.023109 (0.024877) | 0.521849 / 0.275898 (0.245951) | 0.586281 / 0.323480 (0.262801) | 0.006225 / 0.007986 (-0.001760) | 0.005713 / 0.004328 (0.001384) | 0.111507 / 0.004250 (0.107257) | 0.072320 / 0.037052 (0.035267) | 0.551061 / 0.258489 (0.292572) | 0.628034 / 0.293841 (0.334193) | 0.055417 / 0.128546 (-0.073129) | 0.019613 / 0.075646 (-0.056034) | 0.123958 / 0.419271 (-0.295314) | 0.066132 / 0.043533 (0.022600) | 0.504461 / 0.255139 (0.249322) | 0.560428 / 0.283200 (0.277229) | 0.036098 / 0.141683 (-0.105585) | 1.927398 / 1.452155 (0.475243) | 2.015952 / 1.492716 (0.523235) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313065 / 0.018006 (0.295059) | 0.609174 / 0.000490 (0.608684) | 0.008755 / 0.000200 (0.008555) | 0.000120 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040042 / 0.037411 (0.002630) | 0.136053 / 0.014526 (0.121527) | 0.143406 / 0.176557 (-0.033150) | 0.213080 / 0.737135 (-0.524055) | 0.154730 / 0.296338 (-0.141609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.692706 / 0.215209 (0.477497) | 6.952968 / 2.077655 (4.875314) | 3.232023 / 1.504120 (1.727903) | 2.835450 / 1.541195 (1.294256) | 2.933821 / 1.468490 (1.465331) | 0.984712 / 4.584777 (-3.600065) | 6.127651 / 3.745712 (2.381939) | 2.956781 / 5.269862 (-2.313081) | 1.879928 / 4.565676 (-2.685748) | 0.111069 / 0.424275 (-0.313206) | 0.014598 / 0.007607 (0.006991) | 0.871486 / 0.226044 (0.645442) | 8.588500 / 2.268929 (6.319572) | 3.910740 / 55.444624 (-51.533885) | 3.115781 / 6.876477 (-3.760695) | 3.222367 / 2.142072 (1.080294) | 1.229680 / 4.805227 (-3.575547) | 0.232092 / 6.500664 (-6.268572) | 0.097717 / 0.075469 (0.022248) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774193 / 1.841788 (-0.067595) | 19.863087 / 8.074308 (11.788779) | 24.058856 / 10.191392 (13.867464) | 0.214917 / 0.680424 (-0.465507) | 0.028771 / 0.534201 (-0.505430) | 0.544548 / 0.579283 (-0.034735) | 0.655882 / 0.434364 (0.221518) | 0.629110 / 0.540337 (0.088773) | 0.749246 / 1.386936 (-0.637690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4a5ea6a42dcfef1577288b51beeccc0eb124cee \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007075 / 0.011353 (-0.004278) | 0.005195 / 0.011008 (-0.005813) | 0.113043 / 0.038508 (0.074535) | 0.038442 / 0.023109 (0.015333) | 0.336310 / 0.275898 (0.060412) | 0.381888 / 0.323480 (0.058409) | 0.005990 / 0.007986 (-0.001996) | 0.003893 / 0.004328 (-0.000435) | 0.093123 / 0.004250 (0.088872) | 0.058449 / 0.037052 (0.021397) | 0.359463 / 0.258489 (0.100974) | 0.427485 / 0.293841 (0.133644) | 0.041454 / 0.128546 (-0.087092) | 0.013016 / 0.075646 (-0.062630) | 0.372849 / 0.419271 (-0.046422) | 0.059386 / 0.043533 (0.015853) | 0.381398 / 0.255139 (0.126259) | 0.367603 / 0.283200 (0.084403) | 0.033907 / 0.141683 (-0.107775) | 1.628903 / 1.452155 (0.176749) | 1.764131 / 1.492716 (0.271415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298329 / 0.018006 (0.280322) | 0.593030 / 0.000490 (0.592540) | 0.007653 / 0.000200 (0.007453) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025445 / 0.037411 (-0.011966) | 0.112062 / 0.014526 (0.097536) | 0.119863 / 0.176557 (-0.056693) | 0.178389 / 0.737135 (-0.558746) | 0.129934 / 0.296338 (-0.166404) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.532834 / 0.215209 (0.317625) | 5.250908 / 2.077655 (3.173253) | 2.086920 / 1.504120 (0.582800) | 1.799745 / 1.541195 (0.258550) | 1.909648 / 1.468490 (0.441158) | 0.825382 / 4.584777 (-3.759395) | 5.268304 / 3.745712 (1.522592) | 2.533347 / 5.269862 (-2.736515) | 1.730187 / 4.565676 (-2.835490) | 0.099824 / 0.424275 (-0.324451) | 0.012969 / 0.007607 (0.005362) | 0.732234 / 0.226044 (0.506189) | 6.989066 / 2.268929 (4.720138) | 2.873486 / 55.444624 (-52.571138) | 2.274351 / 6.876477 (-4.602125) | 2.311060 / 2.142072 (0.168987) | 1.125366 / 4.805227 (-3.679861) | 0.214522 / 6.500664 (-6.286142) | 0.077579 / 0.075469 (0.002110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670950 / 1.841788 (-0.170838) | 18.131528 / 8.074308 (10.057220) | 21.277823 / 10.191392 (11.086431) | 0.238807 / 0.680424 (-0.441617) | 0.032251 / 0.534201 (-0.501950) | 0.503859 / 0.579283 (-0.075424) | 0.604825 / 0.434364 (0.170461) | 0.555623 / 0.540337 (0.015286) | 0.647301 / 1.386936 (-0.739635) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010857 / 0.011353 (-0.000496) | 0.005581 / 0.011008 (-0.005427) | 0.094346 / 0.038508 (0.055838) | 0.053084 / 0.023109 (0.029975) | 0.457586 / 0.275898 (0.181688) | 0.545475 / 0.323480 (0.221995) | 0.006761 / 0.007986 (-0.001225) | 0.005094 / 0.004328 (0.000765) | 0.095509 / 0.004250 (0.091258) | 0.077182 / 0.037052 (0.040130) | 0.498717 / 0.258489 (0.240228) | 0.542433 / 0.293841 (0.248592) | 0.051547 / 0.128546 (-0.076999) | 0.014633 / 0.075646 (-0.061014) | 0.106843 / 0.419271 (-0.312428) | 0.068459 / 0.043533 (0.024926) | 0.435793 / 0.255139 (0.180654) | 0.475484 / 0.283200 (0.192285) | 0.039495 / 0.141683 (-0.102188) | 1.684906 / 1.452155 (0.232751) | 1.798693 / 1.492716 (0.305976) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279853 / 0.018006 (0.261847) | 0.601016 / 0.000490 (0.600526) | 0.002055 / 0.000200 (0.001855) | 0.000219 / 0.000054 (0.000165) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030935 / 0.037411 (-0.006477) | 0.121197 / 0.014526 (0.106671) | 0.143360 / 0.176557 (-0.033197) | 0.200862 / 0.737135 (-0.536274) | 0.138656 / 0.296338 (-0.157683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613904 / 0.215209 (0.398695) | 6.155422 / 2.077655 (4.077767) | 2.777238 / 1.504120 (1.273118) | 2.473045 / 1.541195 (0.931851) | 2.604470 / 1.468490 (1.135980) | 0.898871 / 4.584777 (-3.685906) | 5.739666 / 3.745712 (1.993954) | 4.719822 / 5.269862 (-0.550040) | 2.727354 / 4.565676 (-1.838322) | 0.108232 / 0.424275 (-0.316043) | 0.013632 / 0.007607 (0.006025) | 0.771802 / 0.226044 (0.545757) | 7.987466 / 2.268929 (5.718537) | 3.609856 / 55.444624 (-51.834768) | 2.974421 / 6.876477 (-3.902056) | 2.956567 / 2.142072 (0.814495) | 1.093792 / 4.805227 (-3.711435) | 0.213369 / 6.500664 (-6.287295) | 0.084486 / 0.075469 (0.009017) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.693855 / 1.841788 (-0.147933) | 18.055027 / 8.074308 (9.980719) | 21.397964 / 10.191392 (11.206571) | 0.240549 / 0.680424 (-0.439875) | 0.031212 / 0.534201 (-0.502989) | 0.513657 / 0.579283 (-0.065626) | 0.651348 / 0.434364 (0.216985) | 0.603740 / 0.540337 (0.063402) | 0.752287 / 1.386936 (-0.634649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f38d00dd40a444ae54c18caa28304ae36b9c3 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007220 / 0.011353 (-0.004133) | 0.004558 / 0.011008 (-0.006451) | 0.116647 / 0.038508 (0.078139) | 0.046845 / 0.023109 (0.023736) | 0.352429 / 0.275898 (0.076531) | 0.429739 / 0.323480 (0.106259) | 0.006620 / 0.007986 (-0.001366) | 0.003731 / 0.004328 (-0.000597) | 0.088683 / 0.004250 (0.084433) | 0.070583 / 0.037052 (0.033530) | 0.366699 / 0.258489 (0.108210) | 0.420730 / 0.293841 (0.126889) | 0.037342 / 0.128546 (-0.091204) | 0.010041 / 0.075646 (-0.065605) | 0.383477 / 0.419271 (-0.035795) | 0.060279 / 0.043533 (0.016746) | 0.349988 / 0.255139 (0.094849) | 0.371423 / 0.283200 (0.088224) | 0.026725 / 0.141683 (-0.114958) | 1.736886 / 1.452155 (0.284731) | 1.812874 / 1.492716 (0.320157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253256 / 0.018006 (0.235250) | 0.563470 / 0.000490 (0.562980) | 0.010475 / 0.000200 (0.010275) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030518 / 0.037411 (-0.006893) | 0.133324 / 0.014526 (0.118798) | 0.137095 / 0.176557 (-0.039461) | 0.202227 / 0.737135 (-0.534909) | 0.144195 / 0.296338 (-0.152143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480870 / 0.215209 (0.265661) | 4.822713 / 2.077655 (2.745058) | 2.124183 / 1.504120 (0.620064) | 1.910733 / 1.541195 (0.369538) | 1.970266 / 1.468490 (0.501776) | 0.624695 / 4.584777 (-3.960082) | 4.459659 / 3.745712 (0.713947) | 2.210123 / 5.269862 (-3.059739) | 1.300520 / 4.565676 (-3.265157) | 0.077096 / 0.424275 (-0.347180) | 0.013333 / 0.007607 (0.005726) | 0.596841 / 0.226044 (0.370797) | 5.917397 / 2.268929 (3.648469) | 2.699397 / 55.444624 (-52.745228) | 2.274833 / 6.876477 (-4.601644) | 2.525376 / 2.142072 (0.383304) | 0.755718 / 4.805227 (-4.049510) | 0.163587 / 6.500664 (-6.337077) | 0.072817 / 0.075469 (-0.002653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.524306 / 1.841788 (-0.317481) | 18.843312 / 8.074308 (10.769004) | 15.694644 / 10.191392 (5.503252) | 0.177400 / 0.680424 (-0.503024) | 0.020104 / 0.534201 (-0.514097) | 0.466421 / 0.579283 (-0.112862) | 0.537274 / 0.434364 (0.102910) | 0.576920 / 0.540337 (0.036583) | 0.718889 / 1.386936 (-0.668047) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007671 / 0.011353 (-0.003682) | 0.004850 / 0.011008 (-0.006158) | 0.090085 / 0.038508 (0.051576) | 0.052023 / 0.023109 (0.028914) | 0.508575 / 0.275898 (0.232677) | 0.590024 / 0.323480 (0.266544) | 0.004564 / 0.007986 (-0.003422) | 0.005345 / 0.004328 (0.001017) | 0.087904 / 0.004250 (0.083653) | 0.064446 / 0.037052 (0.027394) | 0.525625 / 0.258489 (0.267136) | 0.584307 / 0.293841 (0.290466) | 0.037221 / 0.128546 (-0.091325) | 0.010588 / 0.075646 (-0.065059) | 0.098612 / 0.419271 (-0.320659) | 0.059597 / 0.043533 (0.016064) | 0.488064 / 0.255139 (0.232925) | 0.522330 / 0.283200 (0.239131) | 0.030004 / 0.141683 (-0.111679) | 1.732512 / 1.452155 (0.280357) | 1.809027 / 1.492716 (0.316310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218741 / 0.018006 (0.200735) | 0.494946 / 0.000490 (0.494456) | 0.004580 / 0.000200 (0.004380) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034916 / 0.037411 (-0.002495) | 0.133695 / 0.014526 (0.119169) | 0.147964 / 0.176557 (-0.028592) | 0.213210 / 0.737135 (-0.523926) | 0.148850 / 0.296338 (-0.147488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508855 / 0.215209 (0.293646) | 5.065088 / 2.077655 (2.987433) | 2.473110 / 1.504120 (0.968990) | 2.259765 / 1.541195 (0.718570) | 2.359189 / 1.468490 (0.890699) | 0.639082 / 4.584777 (-3.945695) | 4.768195 / 3.745712 (1.022482) | 2.253803 / 5.269862 (-3.016059) | 1.442996 / 4.565676 (-3.122680) | 0.078761 / 0.424275 (-0.345514) | 0.013936 / 0.007607 (0.006329) | 0.625977 / 0.226044 (0.399933) | 6.260817 / 2.268929 (3.991888) | 3.149640 / 55.444624 (-52.294985) | 2.753555 / 6.876477 (-4.122921) | 2.831872 / 2.142072 (0.689799) | 0.781294 / 4.805227 (-4.023933) | 0.169109 / 6.500664 (-6.331555) | 0.075810 / 0.075469 (0.000341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.533282 / 1.841788 (-0.308506) | 19.460579 / 8.074308 (11.386271) | 17.250424 / 10.191392 (7.059032) | 0.193485 / 0.680424 (-0.486939) | 0.020650 / 0.534201 (-0.513551) | 0.472110 / 0.579283 (-0.107173) | 0.532276 / 0.434364 (0.097912) | 0.613152 / 0.540337 (0.072814) | 0.684684 / 1.386936 (-0.702252) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n" ]

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[ "Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files", "Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n" ]

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[ "https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error", "Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq", "> Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq\n\nThe problem has not been solved, I have tried this before, but the problem is the same", "> \r\n\r\n@syngokhan did u solve it? \r\nI am desperate ", "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False) # change this line to -\r\n\r\ndata[\"train\"] = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False)\r\nAfter doing this change you code should run fine.", "> > \r\n> \r\n> @syngokhan did u solve it? I am desperate\r\n\r\nrefer to my earlier comment. you will find the solution." ]

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[ "Hi ! Feel free to re-run your code later, it will resume automatically where you left", "Tried many times in the last 2 weeks, problem remains.", "Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\nfor index in tqdm(range(num_shards)):\r\n ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f\"{index:05d}.parquet\")\r\n````" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ", "Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006384 / 0.011353 (-0.004969) | 0.003788 / 0.011008 (-0.007220) | 0.098524 / 0.038508 (0.060016) | 0.031786 / 0.023109 (0.008677) | 0.307799 / 0.275898 (0.031901) | 0.337329 / 0.323480 (0.013849) | 0.003650 / 0.007986 (-0.004336) | 0.003731 / 0.004328 (-0.000598) | 0.076816 / 0.004250 (0.072566) | 0.041888 / 0.037052 (0.004835) | 0.310702 / 0.258489 (0.052213) | 0.343846 / 0.293841 (0.050005) | 0.027841 / 0.128546 (-0.100705) | 0.008312 / 0.075646 (-0.067334) | 0.320230 / 0.419271 (-0.099042) | 0.047378 / 0.043533 (0.003845) | 0.308683 / 0.255139 (0.053544) | 0.335129 / 0.283200 (0.051930) | 0.096294 / 0.141683 (-0.045389) | 1.485521 / 1.452155 (0.033366) | 1.559868 / 1.492716 (0.067152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197376 / 0.018006 (0.179370) | 0.430461 / 0.000490 (0.429972) | 0.004152 / 0.000200 (0.003953) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023660 / 0.037411 (-0.013751) | 0.103128 / 0.014526 (0.088602) | 0.107549 / 0.176557 (-0.069008) | 0.175934 / 0.737135 (-0.561201) | 0.112210 / 0.296338 (-0.184129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415804 / 0.215209 (0.200595) | 4.216333 / 2.077655 (2.138679) | 1.910354 / 1.504120 (0.406234) | 1.712689 / 1.541195 (0.171494) | 1.754705 / 1.468490 (0.286215) | 0.554647 / 4.584777 (-4.030130) | 3.393592 / 3.745712 (-0.352120) | 1.737504 / 5.269862 (-3.532358) | 1.021213 / 4.565676 (-3.544464) | 0.066908 / 0.424275 (-0.357367) | 0.011446 / 0.007607 (0.003839) | 0.524630 / 0.226044 (0.298585) | 5.243005 / 2.268929 (2.974077) | 2.349685 / 55.444624 (-53.094939) | 2.027457 / 6.876477 (-4.849020) | 2.131053 / 2.142072 (-0.011020) | 0.669070 / 4.805227 (-4.136157) | 0.136317 / 6.500664 (-6.364347) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254102 / 1.841788 (-0.587686) | 13.790492 / 8.074308 (5.716184) | 14.197772 / 10.191392 (4.006380) | 0.143989 / 0.680424 (-0.536434) | 0.016577 / 0.534201 (-0.517624) | 0.375437 / 0.579283 (-0.203846) | 0.398995 / 0.434364 (-0.035369) | 0.445287 / 0.540337 (-0.095050) | 0.538632 / 1.386936 (-0.848304) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006251 / 0.011353 (-0.005101) | 0.004019 / 0.011008 (-0.006989) | 0.077985 / 0.038508 (0.039477) | 0.028705 / 0.023109 (0.005596) | 0.417360 / 0.275898 (0.141462) | 0.463964 / 0.323480 (0.140484) | 0.003489 / 0.007986 (-0.004497) | 0.003032 / 0.004328 (-0.001296) | 0.077953 / 0.004250 (0.073702) | 0.040104 / 0.037052 (0.003051) | 0.405242 / 0.258489 (0.146753) | 0.475029 / 0.293841 (0.181188) | 0.028113 / 0.128546 (-0.100433) | 0.008610 / 0.075646 (-0.067036) | 0.084847 / 0.419271 (-0.334424) | 0.048227 / 0.043533 (0.004694) | 0.417235 / 0.255139 (0.162096) | 0.450470 / 0.283200 (0.167270) | 0.096978 / 0.141683 (-0.044705) | 1.514688 / 1.452155 (0.062533) | 1.560205 / 1.492716 (0.067488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235125 / 0.018006 (0.217119) | 0.409904 / 0.000490 (0.409414) | 0.002474 / 0.000200 (0.002275) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025152 / 0.037411 (-0.012259) | 0.103517 / 0.014526 (0.088991) | 0.110154 / 0.176557 (-0.066402) | 0.161431 / 0.737135 (-0.575704) | 0.114891 / 0.296338 (-0.181448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456077 / 0.215209 (0.240868) | 4.541171 / 2.077655 (2.463517) | 2.297912 / 1.504120 (0.793792) | 2.079337 / 1.541195 (0.538143) | 2.121291 / 1.468490 (0.652801) | 0.560172 / 4.584777 (-4.024605) | 3.421122 / 3.745712 (-0.324590) | 1.764675 / 5.269862 (-3.505186) | 1.043482 / 4.565676 (-3.522195) | 0.067652 / 0.424275 (-0.356623) | 0.011181 / 0.007607 (0.003574) | 0.557232 / 0.226044 (0.331188) | 5.607851 / 2.268929 (3.338922) | 2.783715 / 55.444624 (-52.660909) | 2.380943 / 6.876477 (-4.495534) | 2.378316 / 2.142072 (0.236244) | 0.674356 / 4.805227 (-4.130871) | 0.135912 / 6.500664 (-6.364752) | 0.067009 / 0.075469 (-0.008460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309002 / 1.841788 (-0.532786) | 14.464073 / 8.074308 (6.389765) | 14.418727 / 10.191392 (4.227335) | 0.148486 / 0.680424 (-0.531938) | 0.016650 / 0.534201 (-0.517551) | 0.368786 / 0.579283 (-0.210497) | 0.395026 / 0.434364 (-0.039338) | 0.433565 / 0.540337 (-0.106772) | 0.526603 / 1.386936 (-0.860333) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n" ]

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